Gene methylation in cancer diagnosis

ABSTRACT

The present invention provides DNA biomarker sequences that are differentially methylated in samples from normal individuals and individuals with cancer. The invention further provides methods of identifying differentially methylated DNA biomarker sequences and their use in detecting and diagnosing cancer.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

The present application is a continuation of U.S. patent application Ser. No. 11/809,355, filed May 30, 2007, and claims benefit of priority to U.S. Provisional Patent application No. 60/803,571, filed May 31, 2006, and U.S. Provisional Patent application No. 60/848,543, filed Sep. 28, 2006, each of which are incorporated by reference in their entirety.

BACKGROUND OF THE INVENTION

Human cancer cells typically contain somatically altered genomes, characterized by mutation, amplification, or deletion of critical genes. In addition, the DNA template from human cancer cells often displays somatic changes in DNA methylation. See, e.g., E. R. Fearon, et al, Cell 61:759 (1990); P. A. Jones, et al., Cancer Res. 46:461 (1986); R. Holliday, Science 238:163 (1987); A. De Bustros, et al., Proc. Natl. Acad. Sci. USA 85:5693 (1988); P. A. Jones, et al., Adv. Cancer Res. 54:1 (1990); S. B. Baylin, et al., Cancer Cells 3:383 (1991); M. Makos, et al., Proc. Natl. Acad. Sci. USA 89:1929 (1992); N. Ohtani-Fujita, et al., Oncogene 8:1063 (1993).

DNA methylases transfer methyl groups from the universal methyl donor S-adenosyl methionine to specific sites on the DNA. Several biological functions have been attributed to the methylated bases in DNA. The most established biological function is the protection of the DNA from digestion by cognate restriction enzymes. This restriction modification phenomenon has, so far, been observed only in bacteria.

Mammalian cells, however, possess different methylases that exclusively methylate cytosine residues on the DNA that are 5′ neighbors of guanine (CpG). This methylation has been shown by several lines of evidence to play a role in gene activity, cell differentiation, tumorigenesis, X-chromosome inactivation, genomic imprinting and other major biological processes (Razin, A., H., and Riggs, R. D. eds. in DNA Methylation Biochemistry and Biological Significance, Springer-Verlag, N.Y., 1984).

In eukaryotic cells, methylation of cytosine residues that are immediately 5′ to a guanosine, occurs predominantly in CG poor loci (Bird, A., Nature 321:209 (1986)). In contrast, discrete regions of CG dinucleotides called CG islands (CGi) remain unmethylated in normal cells, except during X-chromosome inactivation and parental specific imprinting (Li, et al., Nature 366:362 (1993)) where methylation of 5′ regulatory regions can lead to transcriptional repression. For example, de novo methylation of the Rb gene has been demonstrated in a small fraction of retinoblastomas (Sakai, et al., Am. J. Hum. Genet., 48:880 (1991)), and a more detailed analysis of the VHL gene showed aberrant methylation in a subset of sporadic renal cell carcinomas (Herman, et al., Proc. Natl. Acad. Sci. U.S.A., 91:9700 (1994)). Expression of a tumor suppressor gene can also be abolished by de novo DNA methylation of a normally unmethylated 5′ CG island. See, e.g., Issa, et al., Nature Genet. 7:536 (1994); Merlo, et al., Nature Med. 1:686 (1995); Herman, et al., Cancer Res., 56:722 (1996); Graff, et al., Cancer Res., 55:5195 (1995); Herman, et al., Cancer Res. 55:4525 (1995).

Identification of the earliest genetic and epigenetic changes in tumorigenesis is a major focus in molecular cancer research. Diagnostic approaches based on identification of these changes can allow implementation of early detection strategies, tumor staging and novel therapeutic approaches targeting these early changes, leading to more effective cancer treatment. The present invention addresses these and other problems.

BRIEF SUMMARY OF THE INVENTION

The present invention provides methods for determining the methylation status of an individual. In one aspect, the methods comprise:

-   -   obtaining a biological sample from an individual; and     -   determining the methylation status of at least one cytosine         within a DNA region in a sample from the individual where the         DNA region is a SEQ ID NO: selected from the group consisting of         213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225,         226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238,         239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251,         252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264,         and 265.

In a further aspect, the methods comprise determining the presence or absence of cancer (including but not limited to breast, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, or prostate cancer or melanoma) in an individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without cancer,         wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without breast         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         breast cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without lung         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of lung         cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without renal         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         renal cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without liver         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         liver cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without ovarian         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         ovarian cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without head         and neck cancer, wherein the comparison of the methylation         status to the threshold value is predictive of the presence or         absence of head and neck cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without thyroid         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         thyroid cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without bladder         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         bladder cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without         cervical cancer, wherein the comparison of the methylation         status to the threshold value is predictive of the presence or         absence of cervical cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without colon         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         colon cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without         endometrial cancer, wherein the comparison of the methylation         status to the threshold value is predictive of the presence or         absence of endometrial cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without         esophageal cancer, wherein the comparison of the methylation         status to the threshold value is predictive of the presence or         absence of esophegeal cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without colon         cancer, wherein the comparison of the methylation status to the         threshold value is predictive of the presence or absence of         colon cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without         prostate cancer, wherein the comparison of the methylation         status to the threshold value is predictive of the presence or         absence of prostate cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine         within a DNA region in a sample from an individual where the DNA         region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,         or 99% identical to, or comprises, a sequence selected from the         group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218,         219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,         232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,         245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,         258, 259, 260, 261, 262, 263, 264, and 265;     -   b) comparing the methylation status of the at least one cytosine         to a threshold value for the biomarker, wherein the threshold         value distinguishes between individuals with and without         melanoma, wherein the comparison of the methylation status to         the threshold value is predictive of the presence or absence of         melanoma in the individual.

With regard to the embodiments, in some embodiments, the determining step comprises determining the methylation status of at least one cytosine in the DNA region corresponding to a nucleotide in a biomarker in the DNA region, wherein the biomarker is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NOs:160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, and 212.

In some embodiments, the determining step comprises determining the methylation status of the DNA region corresponding to a biomarker.

In some embodiments, the sample is from any body fluid, including but not limited to blood serum, blood plasma, fine needle aspirate of the breast, biopsy of the breast, ductal fluid, ductal lavage, feces, urine, sputum, saliva, semen, lavages, biopsy of the lung, bronchial lavage or bronchial brushings. In some embodiments, the sample is from a tumor or polyp. In some embodiments, the sample is a biopsy from lung, kidney, liver, ovarian, head, neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate or skin tissue. In some embodiments, the sample is from cell scrapes, washings, or resected tissues.

In some embodiments, the methylation status of at least one cytosine is compared to the methylation status of a control locus. In some embodiments, the control locus is an endogenous control. In some embodiments, the control locus is an exogenous control.

In some embodiments, the determining step comprises determining the methylation status of at least one cytosine in at least two of the DNA regions.

In a further aspect, the invention provides computer implemented methods for determining the presence or absence of cancer (including but not limited to breast, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate or melanoma) in an individual. In some embodiments, the methods comprise:

-   -   receiving, at a host computer, a methylation value representing         the methylation status of at least one cytosine within a DNA         region in a sample from the individual where the DNA region is         at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%         identical to, or comprises, a sequence selected from the group         consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219,         220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,         233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,         246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258,         259, 260, 261, 262, 263, 264, and 265; and     -   comparing, in the host computer, the methylation value to a         threshold value, wherein the threshold value distinguishes         between individuals with and without cancer (including but not         limited to breast, lung, renal, liver, ovarian, head and neck,         thyroid, bladder, cervical, colon, endometrial, esophageal,         prostate or melanoma), wherein the comparison of the methylation         value to the threshold value is predictive of the presence or         absence of cancer (including but not limited to breast, lung,         renal, liver, ovarian, head and neck, thyroid, bladder,         cervical, colon, endometrial, esophageal, or prostate cancer or         melanoma) in the individual.

In some embodiments, the receiving step comprises receiving at least two methylation values, the two methylation values representing the methylation status of at least one cytosine biomarkers from two different DNA regions; and

-   -   the comparing step comprises comparing the methylation values to         one or more threshold value(s) wherein the threshold value         distinguishes between individuals with and without cancer         (including but not limited to breast, lung, renal, liver,         ovarian, head and neck, thyroid, bladder, cervical, colon,         endometrial, esophageal, prostate or melanoma), wherein the         comparison of the methylation value to the threshold value is         predictive of the presence or absence of cancer (including but         not limited to breast, lung, renal, liver, ovarian, head and         neck, thyroid, bladder, cervical, colon, endometrial,         esophageal, or prostate cancer or melanoma) in the individual.

In another aspect, the invention provides computer program products for determining the presence or absence of cancer (including but not limited to breast, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, or prostate cancer or melanoma) in an individual. In some embodiments, the computer readable products comprise:

-   -   a computer readable medium encoded with program code, the         program code including:         -   program code for receiving a methylation value representing             the methylation status of at least one cytosine within a DNA             region in a sample from the individual where the DNA region             is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or             99% identical to, or comprises, a sequence selected from the             group consisting of SEQ ID NOs: 213, 214, 215, 216, 217,             218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,             230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241,             242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253,             254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and             265; and         -   program code for comparing the methylation value to a             threshold value, wherein the threshold value distinguishes             between individuals with and without cancer (including but             not limited to breast, lung, renal, liver, ovarian, head and             neck, thyroid, bladder, cervical, colon, endometrial,             esophageal, or prostate cancer or melanoma), wherein the             comparison of the methylation value to the threshold value             is predictive of the presence or absence of cancer             (including but not limited to breast, lung, renal, liver,             ovarian, head and neck, thyroid, bladder, cervical, colon,             endometrial, esophageal, or prostate cancer or melanoma) in             the individual.

In a further aspect, the invention provides kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:

-   -   a pair of polynucleotides capable of specifically amplifying at         least a portion of a DNA region where the DNA region is a         sequence selected from the group consisting of SEQ ID NOs: of         213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225,         226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238,         239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251,         252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264,         and 265; and     -   a methylation-dependent or methylation sensitive restriction         enzyme and/or sodium bisulfite.

In some embodiments, the pair of polynucleotides are capable of specifically amplifying a biomarker selected from the group consisting of one or more of SEQ ID NOs: 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, and 212.

In some embodiments, the kits comprise at least two pairs of polynucleotides, wherein each pair is capable of specifically amplifying at least a portion of a different DNA region selected from the group consisting of SEQ ID NOs: of 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265.

In some embodiments, the kits further comprise a detectably labeled polynucleotide probe that specifically detects the amplified biomarker in a real time amplification reaction.

In a further aspect, the invention provides kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:

-   -   sodium bisulfite and polynucleotides to quantify the presence of         the converted methylated and or the converted unmethylated         sequence of at least one cytosine from a DNA region that is         selected from the group consisting of SEQ ID NOs: 213, 214, 215,         216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228,         229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241,         242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254,         255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265.

In a further aspect, the invention provides kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:

-   -   sodium bisulfite, primers and adapters for whole genome         amplification, and polynucleotides to quantify the presence of         the converted methylated and or the converted unmethylated         sequence of at least one cytosine from a DNA region that is         selected from the group consisting of SEQ ID NOs: 213, 214, 215,         216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228,         229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241,         242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254,         255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265.

In another aspect, the methods provide kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:

-   -   a methylation sensing restriction enzymes, primers and adapters         for whole genome amplification, and polynucleotides to quantify         the number of copies of at least a portion of a DNA region where         the DNA region is selected from the group consisting of SEQ ID         NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224,         225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,         238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250,         251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263,         264, and 265.

In a further aspect, the invention provides kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:

-   -   a methylation binding moiety and one or more polynucleotides to         quantify the number of copies of at least a portion of a DNA         region where the DNA region is selected from the group         consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219,         220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,         233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,         246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258,         259, 260, 261, 262, 263, 264, and 265.

DEFINITIONS

“Methylation” refers to cytosine methylation at positions C5 or N4 of cytosine, the N6 position of adenine or other types of nucleic acid methylation. In vitro amplified DNA is unmethylated because in vitro DNA amplification methods do not retain the methylation pattern of the amplification template. However, “unmethylated DNA” or “methylated DNA” can also refer to amplified DNA whose original template was methylated or methylated, respectively.

A “methylation profile” refers to a set of data representing the methylation states of one or more loci within a molecule of DNA from e.g., the genome of an individual or cells or tissues from an individual. The profile can indicate the methylation state of every base in an individual, can comprise information regarding a subset of the base pairs (e.g., the methylation state of specific restriction enzyme recognition sequence) in a genome, or can comprise information regarding regional methylation density of each locus.

“Methylation status” refers to the presence, absence and/or quantity of methylation at a particular nucleotide, or nucleotides within a portion of DNA. The methylation status of a particular DNA sequence (e.g., a DNA biomarker or DNA region as described herein) can indicate the methylation state of every base in the sequence or can indicate the methylation state of a subset of the base pairs (e.g., of cytosines or the methylation state of one or more specific restriction enzyme recognition sequences) within the sequence, or can indicate information regarding regional methylation density within the sequence without providing precise information of where in the sequence the methylation occurs. The methylation status can optionally be represented or indicated by a “methylation value.” A methylation value can be generated, for example, by quantifying the amount of intact DNA present following restriction digestion with a methylation dependent restriction enzyme. In this example, if a particular sequence in the DNA is quantified using quantitative PCR, an amount of template DNA approximately equal to a mock treated control indicates the sequence is not highly methylated whereas an amount of template substantially less than occurs in the mock treated sample indicates the presence of methylated DNA at the sequence. Accordingly, a value, i.e., a methylation value, for example from the above described example, represents the methylation status and can thus be used as a quantitative indicator of methylation status. This is of particular use when it is desirable to compare the methylation status of a sequence in a sample to a threshold value.

A “methylation-dependent restriction enzyme” refers to a restriction enzyme that cleaves or digests DNA at or in proximity to a methylated recognition sequence, but does not cleave DNA at or near the same sequence when the recognition sequence is not methylated. Methylation-dependent restriction enzymes include those that cut at a methylated recognition sequence (e.g., DpnI) and enzymes that cut at a sequence near but not at the recognition sequence (e.g., McrBC). For example, McrBC's recognition sequence is 5′ RmC (N40-3000) RmC 3′ where “R” is a purine and “mC” is a methylated cytosine and “N40-3000” indicates the distance between the two RmC half sites for which a restriction event has been observed. McrBC generally cuts close to one half-site or the other, but cleavage positions are typically distributed over several base pairs, approximately 30 base pairs from the methylated base. McrBC sometimes cuts 3′ of both half sites, sometimes 5′ of both half sites, and sometimes between the two sites. Exemplary methylation-dependent restriction enzymes include, e.g., McrBC (see, e.g., U.S. Pat. No. 5,405,760), McrA, MrrA, and DpnI. One of skill in the art will appreciate that any methylation-dependent restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use in the present invention.

A “methylation-sensitive restriction enzyme” refers to a restriction enzyme that cleaves DNA at or in proximity to an unmethylated recognition sequence but does not cleave at or in proximity to the same sequence when the recognition sequence is methylated. Exemplary methylation-sensitive restriction enzymes are described in, e.g., McClelland et al., Nucleic Acids Res. 22(17):3640-59 (1994) and http://rebase.neb.com. Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when a cytosine within the recognition sequence is methylated at position C⁵ include, e.g., Aat II, Aci I, Acl I, Age I, Alu I, Asc I, Ase I, AsiS I, Bbe I, BsaA I, BsaH I, BsiE I, BsiW I, BsrF I, BssH II, BssK I, BstB I, BstN I, BstU I, Cla I, Eae I, Eag I, Fau I, Fse I, Hha I, HinP1 I, HinC II, Hpa II, Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I, MapAl I, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II, Sap I, Sau3A I, Sfl I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I, and Zra I. Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when an adenosine within the recognition sequence is methylated at position N⁶ include, e.g., Mbo I. One of skill in the art will appreciate that any methylation-sensitive restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use in the present invention. One of skill in the art will further appreciate that a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of a cytosine at or near its recognition sequence may be insensitive to the presence of methylation of an adenosine at or near its recognition sequence. Likewise, a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of an adenosine at or near its recognition sequence may be insensitive to the presence of methylation of a cytosine at or near its recognition sequence. For example, Sau3AI is sensitive (i.e., fails to cut) to the presence of a methylated cytosine at or near its recognition sequence, but is insensitive (i.e., cuts) to the presence of a methylated adenosine at or near its recognition sequence. One of skill in the art will also appreciate that some methylation-sensitive restriction enzymes are blocked by methylation of bases on one or both strands of DNA encompassing of their recognition sequence, while other methylation-sensitive restriction enzymes are blocked only by methylation on both strands, but can cut if a recognition site is hemi-methylated.

A “threshold value that distinguishes between individuals with and without” a particular disease refers to a value or range of values of a particular measurement that can be used to distinguish between samples from individuals with the disease and samples without the disease. Ideally, there is a threshold value or values that absolutely distinguishes between the two groups (i.e., values from the diseased group are always on one side (e.g., higher) of the threshold value and values from the healthy, nog-diseased group are on the other side (e.g., lower) of the threshold value). However, in many instances, threshold values do not absolutely distinguish between diseased and non-diseased samples (for example, when there is some overlap of values generated from diseased and non-diseased samples).

The phrase “corresponding to a nucleotide in a biomarker” refers to a nucleotide in a DNA region that aligns with the same nucleotide (e.g., a cytosine) in a biomarker sequence. Generally, as described herein, biomarker sequences are subsequences of the DNA regions. Sequence comparisons can be performed using any BLAST including BLAST 2.2 algorithm with default parameters, described in Altschul et al., Nuc. Acids Res. 25:3389 3402 (1997) and Altschul et al., J. Mol. Biol. 215:403 410 (1990), respectively. Thus for example, a DNA region or biomarker described herein can correspond to a DNA sequence in a human genome even if there is slight variation between the biomarker or DNA region and the particular human genome in question. Such difference can be the result of slight genetic variation between humans.

“Sensitivity” of a given biomarker refers to the percentage of tumor samples that report a DNA methylation value above a threshold value that distinguishes between tumor and non-tumor samples. The percentage is calculated as follows:

${Sensitivity} = {\left\lbrack \frac{\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {tumor}\mspace{14mu} {samples}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {threshold}} \right)}{\left( {{the}\mspace{14mu} {total}{\mspace{11mu} \;}{number}\mspace{14mu} {of}\mspace{14mu} {tumor}\mspace{14mu} {samples}\mspace{14mu} {tested}} \right)} \right\rbrack \times 100}$

The equation may also be stated as follows:

${Sensitivity} = {\left\lbrack \frac{\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {positive}\mspace{14mu} {samples}} \right)}{\begin{matrix} {\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {positive}\mspace{14mu} {samples}} \right) +} \\ \left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {false}\mspace{14mu} {negative}\mspace{14mu} {samples}} \right) \end{matrix}} \right\rbrack \times 100}$

where true positive is defined as a histology-confirmed tumor sample that reports a DNA methylation value above the threshold value (i.e. the range associated with disease), and false negative is defined as a histology-confirmed tumor sample that reports a DNA methylation value below the threshold value (i.e. the range associated with no disease). The value of sensitivity, therefore, reflects the probability that a DNA methylation measurement for a given biomarker obtained from a known diseased sample will be in the range of disease-associated measurements. As defined here, the clinical relevance of the calculated sensitivity value represents an estimation of the probability that a given biomarker would detect the presence of a clinical condition when applied to a patient with that condition.

“Specificity” of a given biomarker refers to the percentage of non-tumor samples that report a DNA methylation value below a threshold value that distinguishes between tumor and non-tumor samples. The percentage is calculated as follows:

${Specificity} = {\quad {\left\lbrack \frac{\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {non}\text{-}{tumor}\mspace{14mu} {samples}\mspace{14mu} {below}\mspace{14mu} {the}\mspace{14mu} {threshold}} \right)}{\left( {{the}\mspace{14mu} {total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {non}\text{-}{tumor}\mspace{14mu} {samples}\mspace{14mu} {tested}} \right)} \right\rbrack \times 100}}$

The equation may also be stated as follows:

${Specificity} = {\left\lbrack \frac{\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {negative}\mspace{14mu} {samples}} \right)}{\begin{matrix} {\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {negative}\mspace{14mu} {samples}} \right) +} \\ \left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {false}\mspace{14mu} {positive}\mspace{14mu} {samples}} \right) \end{matrix}} \right\rbrack \times 100}$

where true negative is defined as a histology-confirmed non-tumor sample that reports a DNA methylation value below the threshold value (i.e. the range associated with no disease), and false positive is defined as a histology-confirmed non-tumor sample that reports DNA methylation value above the threshold value (i.e. the range associated with disease). The value of specificity, therefore, reflects the probability that a DNA methylation measurement for a given biomarker obtained from a known non-diseased sample will be in the range of non-disease associated measurements. As defined here, the clinical relevance of the calculated specificity value represents an estimation of the probability that a given biomarker would detect the absence of a clinical condition when applied to a patient without that condition.

Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information. This algorithm involves first identifying high scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence, which either match or satisfy some positive-valued threshold score T when aligned with a word of the same length in a database sequence. T is referred to as the neighborhood word score threshold (Altschul et al., supra). These initial neighborhood word hits act as seeds for initiating searches to find longer HSPs containing them. The word hits are extended in both directions along each sequence for as far as the cumulative alignment score can be increased. Cumulative scores are calculated using, for nucleotide sequences, the parameters M (reward score for a pair of matching residues; always >0) and N (penalty score for mismatching residues; always <0). For amino acid sequences, a scoring matrix is used to calculate the cumulative score. Extension of the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment. The BLASTN program (for nucleotide sequences) uses as defaults a wordlength (W) of 11, an expectation (E) of 10, M=5, N=−4 and a comparison of both strands. For amino acid sequences, the BLASTP program uses as defaults a wordlength of 3, and expectation (E) of 10, and the BLOSUM62 scoring matrix (see Henikoff & Henikoff, Proc. Natl. Acad. Sci. USA 89:10915 (1989)) alignments (B) of 50, expectation (E) of 10, M=5, N=−4, and a comparison of both strands.

As used herein, the terms “nucleic acid,” “polynucleotide” and “oligonucleotide” refer to nucleic acid regions, nucleic acid segments, primers, probes, amplicons and oligomer fragments. The terms are not limited by length and are generic to linear polymers of polydeoxyribonucleotides (containing 2-deoxy-D-ribose), polyribonucleotides (containing D-ribose), and any other N-glycoside of a purine or pyrimidine base, or modified purine or pyrimidine bases. These terms include double- and single-stranded DNA, as well as double- and single-stranded RNA.

A nucleic acid, polynucleotide or oligonucleotide can comprise, for example, phosphodiester linkages or modified linkages including, but not limited to phosphotriester, phosphoramidate, siloxane, carbonate, carboxymethylester, acetamidate, carbamate, thioether, bridged phosphoramidate, bridged methylene phosphonate, phosphorothioate, methylphosphonate, phosphorodithioate, bridged phosphorothioate or sulfone linkages, and combinations of such linkages.

A nucleic acid, polynucleotide or oligonucleotide can comprise the five biologically occurring bases (adenine, guanine, thymine, cytosine and uracil) and/or bases other than the five biologically occurring bases. For example, a polynucleotide of the invention can contain one or more modified, non-standard, or derivatized base moieties, including, but not limited to, N⁶-methyl-adenine, N⁶-tert-butyl-benzyl-adenine, imidazole, substituted imidazoles, 5-fluorouracil, 5-bromouraci1,5-chlorouracil, 5-iodouracil, hypoxanthine, xanthine, 4-acetylcytosine, 5-(carboxyhydroxymethyl)uracil, 5-carboxymethylaminomethyl-2-thiouridine, 5-carboxymethylaminomethyluracil, dihydrouracil, beta-D-galactosylqueosine, inosine, N⁶-isopentenyladenine, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N⁶-methyladenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta-D mannosylqueosine, 5′-methoxycarboxymethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid (v), wybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, uracil-5-oxyacetic acidmethylester, 3-(3-amino-3-N-2-carboxypropyl) uracil, (acp3)w, 2,6-diaminopurine, and 5-propynyl pyrimidine. Other examples of modified, non-standard, or derivatized base moieties may be found in U.S. Pat. Nos. 6,001,611; 5,955,589; 5,844,106; 5,789,562; 5,750,343; 5,728,525; and 5,679,785.

Furthermore, a nucleic acid, polynucleotide or oligonucleotide can comprise one or more modified sugar moieties including, but not limited to, arabinose, 2-fluoroarabinose, xylulose, and a hexose.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a general overview of the experimental design of differential methylation screening. A graphical representation of the transcription start site and 5′ structure of one predicted differentially methylated gene is indicated (A). The bar graph (B) indicates the relative local density of purine-CG sequences within this region. The relative position of the DNA microarray feature that reported differential DNA methylation at this locus is indicated by (C). PCR primers were selected to amplify the region indicated by (D). The vertical bars (E and F) represent the microarray DNA methylation measurement representing all breast tumors (E) and all normal breast samples (F).

FIG. 2 illustrates an example of McrBC-based real-time PCR strategy to monitor DNA methylation status. Panel A shows the untreated/treated PCR replicate 1 for amplification of the GHSR locus in a breast tumor sample. The delta Ct (Treated 1—Untreated 1) is 5.38 cycles. Panel B shows the untreated/treated PCR replicate 2 for amplification of the same locus from the same tumor sample. The delta Ct (Treated 2—Untreated 2) is 5.40 cycles. Panel C shows the untreated/treated PCR replicate 1 for amplification of the GHSR locus in a normal breast sample. The delta Ct (Treated 1—Untreated 1) is 0.18 cycles. Panel D shows the untreated/treated PCR replicate 2 for amplification of the same locus from the same normal sample. The delta Ct (Treated 2—Untreated 2) is 0.03 cycles. Tumor samples produce a change in cycle threshold (“delta Ct”) of 1.0 or greater. Normal samples produce a delta Ct of less than 1.0.

FIGS. 3A-B illustrates verification of microarray DNA methylation predictions. Open boxes represent loci that are unmethylated (average delta Ct<1.0), grey boxes represent loci that are methylated (average delta Ct>1 and <2), and black boxes represent loci that are densely methylated (average delta Ct>2).

FIGS. 4A and 4B illustrate validation of DNA methylation differences in biomarkers from independent tumor and normal samples. Boxes are as indicated for FIG. 3.

FIG. 5 illustrates DNA methylation differences in biomarkers from a larger panel of breast tumor, normal breast and normal peripheral blood samples. Boxes are as indicated for FIG. 3.

FIG. 6 illustrates demonstration of threshold adjustment for determining sensitivity and specificity.

FIG. 7 illustrates bisulfite sequencing confirmation of differential DNA methylation.

FIG. 8 illustrates the correlation between qPCR based DNA methylation measurements and DNA methylation occupancies determined by bisulfite sequencing. Primers were designed to amplify approximately 150 bp amplicons within the region of three loci that were analyzed by qPCR as described above. The loci included feature ID halp_(—)39189 (locus number 2), ha1g_(—)00644 (locus number 3) and ha1p_(—)104423 (locus number 12). For each amplicon, products were amplified from three normal breast DNA samples that reported average dCt values <0.5, three normal breast DNA samples that reported average dCt values between 0.5 and 1.0, and three breast tumor DNA samples that reported average dCt values greater than 1.0. Amplicons were purified and cloned using TA cloning kits (Invitrogen). At least 29 independent clones were sequenced per amplicon, per locus. The graph shows the median 5-methylcytosine content for all sequenced clones per amplicon plotted against the average dCt value for that locus in the same DNA sample. The dashed vertical line represents the dCt=1.0 threshold used to indicate a positive qPCR measurement for DNA methylation detection.

FIG. 9 illustrates an example of selection of a potentially differentially methylated region based on an analysis of CpG density (identification of a CG island).

FIG. 10A illustrates the frequency of differential DNA methylation of 16 loci in stage I breast tumors relative to stage II-III breast tumors. The 16 loci include those listed in Table 5.

FIG. 10B illustrates the DNA methylation density of three selected loci relative to tumor stage. The averaged approximate percent depletion of methylated molecules by McrBC was calculated to determine the load of methylated molecules in each sample [1−(1/2̂delta Ct (McrBC digested—Mock treated))*100]. Data are plotted (from left to right) for normal breast samples, stage I tumors, stage IIA tumors, stage IIB tumors and stage III tumors.

FIG. 11A illustrates the differential DNA methylation of four selected loci in breast tumor, normal breast tissue and peripheral blood from a cancer-free woman. Each data point represents the averaged delta Ct value for an independent clinical sample.

FIG. 11B illustrates ROC curve analyses of the four loci depicted in FIG. 11A. Sensitivity (percentage of tumor samples scoring above a methylation threshold) and specificity (percentage of non-tumor samples scoring below that same threshold) were calculated for all observed delta Ct values.

FIG. 12 illustrates the analysis of DNA methylation of four selected loci by bisulfite sequencing. Analyzed loci included locus number 2 (A, B), 3 (C, D), 4 (E, F) and 12 (G, H). Bisulfite sequencing was performed. The average number of molecules sequenced for each locus within each sample was 587. The calculated DNA methylation density (number of methylated CpGs divided by the total number of CpGs sequenced) for each sample is plotted versus the qPCR DNA methylation measurement for the same sample (A, C, E, G). In addition, the percent methylation occupancy at each analyzed CpG dinucleotide is shown (B, D, F, H). Analyzed samples included normal breast tissues (open circles), adjacent histology normal breast tissues (filled circles) and breast tumors (filled squares).

FIG. 13 illustrates the correlation between DNA hypermethylation and gene expression. Transcription of GHSR (locus number 2), MGA (locus number 4), and NFX1 (locus number 12) were analyzed by RT-PCR. Serial dilutions of cDNA from normal breast tissue and four breast tumors were used as template as indicated. GAPDH expression was analyzed as an internal control for each sample. The DNA methylation measurement (qPCR) for each locus in each tumor sample is indicated (−average dCt<1.0, +average dCt>1.0 but <2.0, and ++average dCt>2.0).

FIG. 14 illustrates the comparison of DNA methylation detection in fine needle aspirate (% POSITIVE FNA) samples relative to unmatched primary breast tumor samples (% POSITIVE TUMOR). Each sample was scored as positive if the average dCt was ≧1.0, as described in Example 3. Analyzed samples included 7 FNA samples and at least 14 primary breast tumor samples.

DETAILED DESCRIPTION OF THE INVENTION I. Introduction

The present invention is based, in part, on the discovery that sequences in certain DNA regions are methylated in cancer cells, but not normal cells. The inventors have found that methylation within the DNA regions described herein are associated with breast cancer, particularly ductal carcinoma, as well as a number of other cancers.

In view of this discovery, the inventors have recognized that methods for detecting the biomarker sequences and DNA regions comprising the biomarker sequences as well as sequences adjacent to the biomarkers that contain a significant amount of CG subsequences, methylation of the DNA regions, and/or expression of the genes regulated by the DNA regions can be used to detect cancer cells. Detecting cancer cells allows for diagnostic tests that detect disease, assess the risk of contracting disease, determining a predisposition to disease, stage disease, diagnose disease, monitor disease, and/or aid in the selection of treatment for a person with disease.

II. Methylation Biomarkers

In some embodiments, the presence or absence or quantity of methylation of the chromosomal DNA within a DNA region or portion thereof (e.g., at least one cytosine) selected from SEQ ID Nos: 213-265 is detected. Portions of the DNA regions described herein will comprise at least one potential methylation site (i.e., a cytosine) and can in some embodiments generally comprise 2, 3, 4, 5, 10, or more potential methylation sites. In some embodiments, the methylation status of all cytosines within at least 20, 50, 100, 200, 500 or more contiguous base pairs of the DNA region are determined.

In some embodiments, the methylation of more than one DNA region (or portion thereof) is detected. In some embodiments, the methylation status at least one cytosine in 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52 or 53 of the DNA regions is determined.

In some embodiments of the invention, the methylation of a DNA region or portion thereof is determined and then compared (e.g., normalized) to the methylation of a control locus. Typically the control locus will have a known, relatively constant, methylation status. For example, the control sequence can be previously determined to have no, some, or a high amount of methylation, thereby providing a relative constant value to control for error in detection methods, etc., unrelated to the presence or absence of cancer. In some embodiments, the control locus is endogenous, i.e., is part of the genome of the individual sampled. For example, in mammalian cells, the testes-specific histone 2B gene (hTH2B in human) gene is known to be methylated in all somatic tissues except testes. Alternatively, the control locus can be an exogenous locus, i.e., a DNA sequence spiked into the sample in a known quantity and having a known methylation status.

A DNA region comprises a nucleic acid including one or more methylation sites of interest (e.g., a cytosine, a “microarray feature” as exemplified in FIG. 1C, or an amplicon amplified from select primers as exemplified in FIG. 1D) and flanking nucleic acid sequences (i.e., “wingspan”) of up to 4 kilobases (kb) in either or both of the 3′ or 5′ direction from the amplicon. This range corresponds to the lengths of DNA fragments obtained by randomly shearing the DNA before screening for differential methylation between DNA in two or more samples (e.g., carrying out methods used to initially identify differentially methylated sequences as described in the Examples, below). In some embodiments, the wingspan of the one or more DNA regions is about 0.5 kb, 0.75 kb, 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb.

In some cases, the DNA region comprises more nucleotides than simply the wingspan of the discovery method because the relevant microarray feature or amplicon reside in a larger region of higher CG density in the chromosome. This range corresponds to identified lengths of nucleic acid sequences having higher CG density (e.g., a “CG island”) than flanking nucleic acid sequences (e.g., “local minimum” CG density) (see, for example, FIG. 8). DNA regions having extended sequences of heightened CG density include, for example, sequences 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265 (see, Table 2 and section “SEQUENCE LISTING”).

The methylation sites in a DNA region can reside in non-coding transcriptional control sequences (e.g., promoters, enhancers, etc.) or in coding sequences, including introns and exons of the designated genes listed in Tables 1 and 2 and in section “SEQUENCE LISTING.” In some embodiments, the methods comprise detecting the methylation status in the promoter regions (e.g., comprising the nucleic acid sequence that is about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb 5′ from the transcriptional start site through to the transcriptional start site) of one or more of the genes identified in Tables 1 and 2 and in the “SEQUENCE LISTING” section.

The DNA regions of the invention also include naturally occurring variants, including for example, variants occurring in different subject populations and variants arising from single nucleotide polymorphisms (SNPs). Variants include nucleic acid sequences from the same DNA region (e.g., as set forth in Tables 1 and 2 and in the “SEQUENCE LISTING” section) sharing at least 90%, 95%, 98%, 99% sequence identity, i.e., having one or more deletions, additions, substitutions, inverted sequences, etc., relative to the DNA regions described herein.

III. Methods for Determining Methylation

Any method for detecting DNA methylation can be used in the methods of the present invention.

In some embodiments, methods for detecting methylation include randomly shearing or randomly fragmenting the genomic DNA, cutting the DNA with a methylation-dependent or methylation-sensitive restriction enzyme and subsequently selectively identifying and/or analyzing the cut or uncut DNA. Selective identification can include, for example, separating cut and uncut DNA (e.g., by size) and quantifying a sequence of interest that was cut or, alternatively, that was not cut. See, e.g., U.S. Pat. No. 7,186,512. Alternatively, the method can encompass amplifying intact DNA after restriction enzyme digestion, thereby only amplifying DNA that was not cleaved by the restriction enzyme in the area amplified. See, e.g., U.S. patent application Ser. Nos. 10/971,986; 11/071,013; and 10/971,339. In some embodiments, amplification can be performed using primers that are gene specific. Alternatively, adaptors can be added to the ends of the randomly fragmented DNA, the DNA can be digested with a methylation-dependent or methylation-sensitive restriction enzyme, intact DNA can be amplified using primers that hybridize to the adaptor sequences. In this case, a second step can be performed to determine the presence, absence or quantity of a particular gene in an amplified pool of DNA. In some embodiments, the DNA is amplified using real-time, quantitative PCR.

In some embodiments, the methods comprise quantifying the average methylation density in a target sequence within a population of genomic DNA. In some embodiments, the method comprises contacting genomic DNA with a methylation-dependent restriction enzyme or methylation-sensitive restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved; quantifying intact copies of the locus; and comparing the quantity of amplified product to a control value representing the quantity of methylation of control DNA, thereby quantifying the average methylation density in the locus compared to the methylation density of the control DNA.

The quantity of methylation of a locus of DNA can be determined by providing a sample of genomic DNA comprising the locus, cleaving the DNA with a restriction enzyme that is either methylation-sensitive or methylation-dependent, and then quantifying the amount of intact DNA or quantifying the amount of cut DNA at the DNA locus of interest. The amount of intact or cut DNA will depend on the initial amount of genomic DNA containing the locus, the amount of methylation in the locus, and the number (i.e., the fraction) of nucleotides in the locus that are methylated in the genomic DNA. The amount of methylation in a DNA locus can be determined by comparing the quantity of intact DNA or cut DNA to a control value representing the quantity of intact DNA or cut DNA in a similarly-treated DNA sample. The control value can represent a known or predicted number of methylated nucleotides. Alternatively, the control value can represent the quantity of intact or cut DNA from the same locus in another (e.g., normal, non-diseased) cell or a second locus.

By using at least one methylation-sensitive or methylation-dependent restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved and subsequently quantifying the remaining intact copies and comparing the quantity to a control, average methylation density of a locus can be determined. If the methylation-sensitive restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be directly proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Similarly, if a methylation-dependent restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be inversely proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Such assays are disclosed in, e.g., U.S. patent application Ser. No. 10/971,986.

Kits for the above methods can include, e.g., one or more of methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, amplification (e.g., PCR) reagents, probes and/or primers.

Quantitative amplification methods (e.g., quantitative PCR or quantitative linear amplification) can be used to quantify the amount of intact DNA within a locus flanked by amplification primers following restriction digestion. Methods of quantitative amplification are disclosed in, e.g., U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972,602, as well as in, e.g., Gibson et al., Genome Research 6:995-1001 (1996); DeGraves, et al., Biotechniques 34(1):106-10, 112-5 (2003); Deiman B, et al., Mol. Biotechnol. 20(2):163-79 (2002). Amplifications may be monitored in “real time.”

Additional methods for detecting DNA methylation can involve genomic sequencing before and after treatment of the DNA with bisulfite. See, e.g., Frommer et al., Proc. Natl. Acad. Sci. USA 89:1827-1831 (1992). When sodium bisulfite is contacted to DNA, unmethylated cytosine is converted to uracil, while methylated cytosine is not modified.

In some embodiments, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA is used to detect DNA methylation. See, e.g., Sadri & Hornsby, Nucl. Acids Res. 24:5058-5059 (1996); Xiong & Laird, Nucleic Acids Res. 25:2532-2534 (1997).

In some embodiments, a MethyLight assay is used alone or in combination with other methods to detect DNA methylation (see, Eads et al., Cancer Res. 59:2302-2306 (1999)). Briefly, in the MethyLight process genomic DNA is converted in a sodium bisulfite reaction (the bisulfite process converts unmethylated cytosine residues to uracil). Amplification of a DNA sequence of interest is then performed using PCR primers that hybridize to CpG dinucleotides. By using primers that hybridize only to sequences resulting from bisulfite conversion of unmethylated DNA, (or alternatively to methylated sequences that are not converted) amplification can indicate methylation status of sequences where the primers hybridize. Similarly, the amplification product can be detected with a probe that specifically binds to a sequence resulting from bisulfite treatment of a unmethylated (or methylated) DNA. If desired, both primers and probes can be used to detect methylation status. Thus, kits for use with MethyLight can include sodium bisulfite as well as primers or detectably-labeled probes (including but not limited to Taqman or molecular beacon probes) that distinguish between methylated and unmethylated DNA that have been treated with bisulfite. Other kit components can include, e.g., reagents necessary for amplification of DNA including but not limited to, PCR buffers, deoxynucleotides; and a thermostable polymerase.

In some embodiments, a Ms-SNuPE (Methylation-sensitive Single Nucleotide Primer Extension) reaction is used alone or in combination with other methods to detect DNA methylation (see, Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531 (1997)). The Ms-SNuPE technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA, followed by single-nucleotide primer extension (Gonzalgo & Jones, supra). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific for bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site(s) of interest.

Typical reagents (e.g., as might be found in a typical Ms-SNuPE-based kit) for Ms-SNuPE analysis can include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE primers for a specific gene; reaction buffer (for the Ms-SNuPE reaction); and detectably-labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery regents or kit (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.

In some embodiments, a methylation-specific PCR (“MSP”) reaction is used alone or in combination with other methods to detect DNA methylation. An MSP assay entails initial modification of DNA by sodium bisulfite, converting all unmethylated, but not methylated, cytosines to uracil, and subsequent amplification with primers specific for methylated versus unmethylated DNA. See, Herman et al., Proc. Natl. Acad. Sci. USA 93:9821-9826, (1996); U.S. Pat. No. 5,786,146.

Additional methylation detection methods include, but are not limited to, methylated CpG island amplification (see, Toyota et al., Cancer Res. 59:2307-12 (1999)) and those described in, e.g., U.S. Patent Publication 2005/0069879; Rein, et al. Nucleic Acids Res. 26 (10): 2255-64 (1998); Olek, et al. Nat. Genet. 17(3): 275-6 (1997); and PCT Publication No. WO 00/70090.

It is well known that methylation of genomic DNA can affect expression (transcription and/or translation) of nearby gene sequences. Therefore, in some embodiments, the methods include the step of correlating the methylation status of at least one cytosine in a DNA region with the expression of nearby coding sequences, as described in Tables 1 and 2 and in the “SEQUENCE LISTING” section. For example, expression of gene sequences within about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb in either the 3′ or 5′ direction from the cytosine of interest in the DNA region can be detected. Methods for measuring transcription and/or translation of a particular gene sequence are well known in the art. See, for example, Ausubel, Current Protocols in Molecular Biology, 1987-2006, John Wiley & Sons; and Sambrook and Russell, Molecular Cloning: A Laboratory Manual, 3rd Edition, 2000, Cold Spring Harbor Laboratory Press. In some embodiments, the gene or protein expression of a gene in Tables 1 and 2 and in the “SEQUENCE LISTING” section is compared to a control, for example, the methylation status in the DNA region and/or the expression of a nearby gene sequence, and/or the same gene sequence from a sample from an individual known to be negative for cancer or known to be positive for cancer, or to an expression level that distinguishes between cancer and noncancer states. Such methods, like the methods of detecting methylation described herein, are useful in providing diagnosis, prognosis, etc., of breast cancer.

In some embodiments, the methods further comprise the step of correlating the methylation status and expression of one or more of the gene regions identified in Tables 1 and 2 and in the “SEQUENCE LISTING” section.

IV. CANCER DETECTION

The present biomarkers and methods can be used in the detection, diagnosis, prognosis, classification, and treatment of a number of types of cancers. A cancer at any stage of progression can be detected, such as primary, metastatic, and recurrent cancers. Information regarding numerous types of cancer can be found, e.g., from the American Cancer Society (available on the worldwide web at cancer.org), or from, e.g., Harrison's Principles of Internal Medicine, Kaspar, et al., eds., 16th Edition, 2005, McGraw-Hill, Inc. Exemplary cancers that can be detected include, e.g., breast cancers, including ductal carcinoma, as well as lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, or prostate cancer or melanoma.

The present invention provides methods for determining whether or not a mammal (e.g., a human) has cancer, i.e., whether or not a biological sample taken from a mammal contains cancerous cells, estimating the risk or likelihood of a mammal developing cancer, classifying cancer types and stages, and monitoring the efficacy of anti-cancer treatment or selecting the appropriate anti-cancer treatment in a mammal with cancer. Such methods are based on the discovery that cancer cells have a different methylation status than normal cells in the DNA regions described in the invention. Accordingly, by determining whether or not a cell contains differentially methylated sequences in the DNA regions as described herein, it is possible to determine whether or not the cell is cancerous.

In numerous embodiments of the present invention, the presence of methylated nucleotides in the diagnostic biomarker sequences of the invention is detected in a biological sample, thereby detecting the presence or absence of cancerous cells in the mammal from which the biological sample was taken. In some embodiments, the biological sample comprises a tissue sample from a tissue suspected of containing cancerous cells. For example, in an individual suspected of having cancer, breast tissue, lymph tissue, lung tissue, brain tissue, or blood can be evaluated. Alternatively, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate, or skin tissue can be evaluated. The tissue or cells can be obtained by any method known in the art including, e.g., by surgery, biopsy, phlebotomy, swab, nipple discharge, stool, etc. In other embodiments, a tissue sample known to contain cancerous cells, e.g., from a tumor, will be analyzed for the presence or quantity of methylation at one or more of the diagnostic biomarkers of the invention to determine information about the cancer, e.g., the efficacy of certain treatments, the survival expectancy of the individual, etc. In some embodiments, the methods will be used in conjunction with additional diagnostic methods, e.g., detection of other cancer biomarkers, etc.

The methods of the invention can be used to evaluate individuals known or suspected to have cancer or as a routine clinical test, i.e., in an individual not necessarily suspected to have cancer.

Further, the present methods may be used to assess the efficacy of a course of treatment. For example, the efficacy of an anti-cancer treatment can be assessed by monitoring DNA methylation of the biomarker sequences described herein over time in a mammal having cancer. For example, a reduction or absence of methylation in any of the diagnostic biomarkers of the invention in a biological sample taken from a mammal following a treatment, compared to a level in a sample taken from the mammal before, or earlier in, the treatment, indicates efficacious treatment.

The methods detecting cancer can comprise the detection of one or more other cancer-associated polynucleotide or polypeptides sequences. Accordingly, detection of methylation of any one or more of the diagnostic biomarkers of the invention can be used either alone, or in combination with other biomarkers, for the diagnosis or, prognosis of cancer.

The methods of the present invention can be used to determine the optimal course of treatment in a mammal with cancer. For example, the presence of methylated DNA within any of the diagnostic biomarkers of the invention or an increased quantity of methylation within any of the diagnostic biomarkers of the invention can indicate a reduced survival expectancy of a mammal with cancer, thereby indicating a more aggressive treatment for the mammal. In addition, a correlation can be readily established between the presence, absence or quantity of methylation at a diagnostic biomarker, as described herein, and the relative efficacy of one or another anti-cancer agent. Such analyses can be performed, e.g., retrospectively, i.e., by detecting methylation in one or more of the diagnostic genes in samples taken previously from mammals that have subsequently undergone one or more types of anti-cancer therapy, and correlating the known efficacy of the treatment with the presence, absence or levels of methylation of one or more of the diagnostic biomarkers.

In making a diagnosis, prognosis, risk assessment or classification, in monitoring disease, or in determining the most beneficial course of treatment based on the presence or absence of methylation in at least one of the diagnostic biomarkers, the quantity of methylation may be compared to a threshold value that distinguishes between one diagnosis, prognosis, risk assessment, classification, etc., and another. For example, a threshold value can represent the degree of methylation found at a particular DNA region that adequately distinguishes between breast cancer samples and normal breast samples with a desired level of sensitivity and specificity. It is understood that a threshold value will likely vary depending on the assays used to measure methylation, but it is also understood that it is a relatively simple matter to determine a threshold value or range by measuring methylation of a DNA sequence in diseased and normal samples using the particular desired assay and then determining a value that distinguishes at least a majority of the cancer samples from a majority of non-cancer samples. An example of this is shown in FIG. 6 and the accompanying text in the examples. If methylation of two or more DNA regions is detected, two or more different threshold values (one for each DNA region) will often, but not always, be used. Comparisons between a quantity of methylation of a sequence in a sample and a threshold value in any way known in the art. For example; a manual comparison can be made or a computer can compare and analyze the values to detect disease, assess the risk of contracting disease, determining a predisposition to disease, stage disease, diagnose disease, monitor, or aid in the selection of treatment for a person with disease.

In some embodiments, threshold values provide at least a specified sensitivity and specificity for detection of a particular cancer type. In some embodiments, the threshold value allows for at least a 50%, 60%, 70%, or 80% sensitivity and specificity for detection of a specific cancer, e.g., breast, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate cancer or melanoma. More detail regarding specificity and sensitivity for various cancers can be found in, e.g., Tables 5-6, and 8-20.

In embodiments involving prognosis of cancer (including, for example, the prediction of progression of non-malignant lesions to invasive carcinoma, prediction of metastasis, prediction of disease recurrance or prediction of a response to a particular treatment), in some embodiments, the threshold value is set such that there is at least 10, 20, 30, 40, 50, 60, 70, 80% or more sensitivity and at least 70% specificity with regard to detecting cancer.

In some embodiments, the methods comprise recording a diagnosis, prognosis, risk assessment or classification, based on the methylation status determined from an individual. Any type of recordation is contemplated, including electronic recordation, e.g., by a computer.

V. Kits

This invention also provides kits for the detection and/or quantification of the diagnostic biomarkers of the invention, or expression or methylation thereof using the methods described herein.

For kits for detection of methylation, the kits of the invention can comprise at least one polynucleotide that hybridizes to at least one of the diagnostic biomarker sequences of the invention and at least one reagent for detection of gene methylation. Reagents for detection of methylation include, e.g., sodium bisulfite, polynucleotides designed to hybridize to sequence that is the product of a biomarker sequence of the invention if the biomarker sequence is not methylated (e.g., containing at least one C→U conversion), and/or a methylation-sensitive or methylation-dependent restriction enzyme. The kits can provide solid supports in the form of an assay apparatus that is adapted to use in the assay. The kits may further comprise detectable labels, optionally linked to a polynucleotide, e.g., a probe, in the kit. Other materials useful in the performance of the assays can also be included in the kits, including test tubes, transfer pipettes, and the like. The kits can also include written instructions for the use of one or more of these reagents in any of the assays described herein.

In some embodiments, the kits of the invention comprise one or more (e.g., 1, 2, 3, 4, or more) different polynucleotides capable of specifically amplifying at least a portion of a DNA region where the DNA region is a sequence selected from the group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265. Optionally, one or more detectably-labeled polypeptide capable of hybridizing to the amplified portion can also be included in the kit. In some embodiments, the kits comprise sufficient primers to amplify 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different DNA regions or portions thereof, and optionally include detectably-labeled polynucleotides capable of hybridizing to each amplified DNA region or portion thereof. The kits further can comprise a methylation-dependent or methylation sensitive restriction enzyme and/or sodium bisulfite.

In some embodiments, the kits comprise sodium bisulfite, primers and adapters (e.g., oligonucleotides that can be ligated or otherwise linked to genomic fragments) for whole genome amplification, and polynucleotides (e.g., detectably-labeled polynucleotoides) to quantify the presence of the converted methylated and or the converted unmethylated sequence of at least one cytosine from a DNA region that is selected from the group consisting of SEQ ID NOs:213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265.

In some embodiments, the kits comprise a methylation sensing restriction enzymes (e.g., a methylation-dependent restriction enzyme and/or a methylation-sensitive restriction enzyme), primers and adapters for whole genome amplification, and polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265.

In some embodiments, the kits comprise a methylation binding moiety and one or more polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265. A methylation binding moiety refers to a molecule (e.g., a polypeptide) that specifically binds to methyl-cytosine. Examples include restriction enzymes or fragments thereof that lack DNA cutting activity but retain the ability to bind methylated DNA, antibodies that specifically bind to methylated DNA, etc.).

VI. Computer-Based Methods

The calculations for the methods described herein can involve computer-based calculations and tools. For example, a methylation value for a DNA region or portion thereof can be compared by a computer to a threshold value, as described herein. The tools are advantageously provided in the form of computer programs that are executable by a general purpose computer system (referred to herein as a “host computer”) of conventional design. The host computer may be configured with many different hardware components and can be made in many dimensions and styles (e.g., desktop PC, laptop, tablet PC, handheld computer, server, workstation, mainframe). Standard components, such as monitors, keyboards, disk drives, CD and/or DVD drives, and the like, may be included. Where the host computer is attached to a network, the connections may be provided via any suitable transport media (e.g., wired, optical, and/or wireless media) and any suitable communication protocol (e.g., TCP/IP); the host computer may include suitable networking hardware (e.g., modem, Ethernet card, WiFi card). The host computer may implement any of a variety of operating systems, including UNIX, Linux, Microsoft Windows, MacOS, or any other operating system.

Computer code for implementing aspects of the present invention may be written in a variety of languages, including PERL, C, C++, Java, JavaScript, VBScript, AWK, or any other scripting or programming language that can be executed on the host computer or that can be compiled to execute on the host computer. Code may also be written or distributed in low level languages such as assembler languages or machine languages.

The host computer system advantageously provides an interface via which the user controls operation of the tools. In the examples described herein, software tools are implemented as scripts (e.g., using PERL), execution of which can be initiated by a user from a standard command line interface of an operating system such as Linux or UNIX. Those skilled in the art will appreciate that commands can be adapted to the operating system as appropriate. In other embodiments, a graphical user interface may be provided, allowing the user to control operations using a pointing device. Thus, the present invention is not limited to any particular user interface.

Scripts or programs incorporating various features of the present invention may be encoded on various computer readable media for storage and/or transmission. Examples of suitable media include magnetic disk or tape, optical storage media such as compact disk (CD) or DVD (digital versatile disk), flash memory, and carrier signals adapted for transmission via wired, optical, and/or wireless networks conforming to a variety of protocols, including the Internet.

EXAMPLES Example 1 Identification of Breast Cancer DNA Methylation Biomarkers

Loci that are differentially methylated in breast tumors relative to matched adjacent histologically normal breast tissue were identified using a DNA microarray-based technology platform (U.S. Pat. No. 7,186,512) that utilizes the methylation-dependent restriction enzyme McrBC. In this discovery phase, 10 infiltrating ductal breast carcinomas (9 Stage II, 1 Stage III) and 10 matched adjacent histologically normal breast tissue samples were analyzed. Purified genomic DNA from each sample (60 μg) was randomly sheared to a range of 1 to 4 kb. The sheared DNA of each sample was then split into four equal portions of 15 μg each. Two portions were digested with McrBC under the following conditions: 15 μg sheared genomic DNA, 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 120 units of McrBC enzyme (New England Biolabs) in a total volume of 600 μL at 37° C. for approximately 12 hours. These two portions represent a technical replicate of McrBC digestion (Treated 1 and Treated 2). The remaining two 15 μg portions were mock treated under identical conditions with the exception that 12 μL of sterile 50% glycerol were added instead of McrBC enzyme. These two portions represent a technical replicate of mock treatment (Untreated 1 and Untreated 2). All reactions were treated with 5 μL proteinase K (50 mg/mL) for 1 hour at 50° C., and precipitated with EtOH under standard conditions. Pellets were washed twice with 70% EtOH, dried and resuspended in 30 μL H2O, Samples were then resolved on a 1% low melting point SeaPlaque GTG Agarose gel (Cambridge Bio Sciences). Untreated 1 and Treated 1 portions were resolved side-by-side, as were Untreated 2 and Treated 2 portions. 1 kb DNA sizing ladder was resolved adjacent to each untreated/treated pair to guide accurate gel slice excision. Gels were visualized with long-wave UV, and gel slices including DNA within the modal size range of the untreated fraction (approximately 1-4 kb) were excised with a clean razor blade. DNA was extracted from gel slices using gel extraction kits (Qiagen).

McrBC recognizes a pair of methylated cytosine residues in the context 5′-Pu^(m)C (N₄₀₋₂₀₀₀) Pu^(m)C-3′ (where Pu=A or G, ^(m)C=5-methylcytosine, and N=any nucleotide), and cleaves within approximately 30 base-pairs from one of the methylated cytosine residues. Therefore, loci that include high local densities of Pu^(m)C will be cleaved to a greater extent than loci that include low local densities of Pu^(m)C. Since Untreated and Treated portions were resolved by agarose gel electrophoresis, and DNA within the modal size range of the Untreated portions were excised and gel extracted, the Untreated portions represent the entire fragmented genome of the sample while the Treated portions are depleted of DNA fragments including Pu^(m)C. Fractions were analyzed using a duplicated dye swap microarray hybridization paradigm. For example, equal mass (200 ng) of Untreated 1 and Treated I fraction DNA were used as template for labeling with Cy3 and Cy5, respectively, and hybridized to a DNA microarray (described below). Equal mass (200 ng) of the same Untreated 1 and Treated 1 fraction DNA were used as template for labeling with Cy5 and Cy3, respectively, and hybridized to a second DNA microarray (these two hybridizations represent a dye swap of Untreated 1/Treated 1 fractions). Equal mass (200 ng) of Untreated 2 and Treated 2 fraction DNA were used as template for labeling with Cy3 and Cy5, respectively, and hybridized to a third DNA microarray. Finally, equal mass (200 ng) of Untreated 2 and Treated 2 fraction DNA were used as template for labeling with Cy5 and Cy3, respectively, and hybridized to a fourth DNA microarray (the final two hybridizations represent a technical replicate of the first dye swap). All 20 DNA samples (10 tumor samples and 10 adjacent normal samples) were analyzed in this way. Therefore, the discovery experiment included a total of 80 microarray hybridizations.

The microarray described in this Example consists of 85,176 features. Each 60mer oligonucleotide feature is represented by four replicates per microarray slide, yielding a total of 21,294 unique feature probes. The features represent 19,595 randomly selected human transcriptional start sites (TSS) representing 79% of the identified human genes, 1395 GenBank BAC annotated CG islands (CGi), 161 features spanning ˜165 kb along the MTAPase/CDKN2A/B locus on chromosome 9, 66 additional features dedicated to cancer gene promoters, and 77 features designed as copy number (HERV, LINEs, SINE) and other controls. Together, the TSS features and CGi features scan more than 9000 UCSC annotated human CG islands.

Following statistical analysis of these datasets, loci that were predicted to be differentially methylated in at least 70% of tumors relative to normal tissues were identified. As described in the Examples below, differential DNA methylation of a collection of 53 loci identified by the microarray discovery experiment described herein was verified within the discovery panel of 10 infiltrating ductal breast carcinomas relative to 10 matched adjacent histologically normal breast samples, as well as validated in larger panels of independent infiltrating ductal breast carcinomas, normal breast samples and normal female peripheral blood samples. Tables 1 and 2 and the “SEQUENCE LISTING” section list the unique microarray feature identifier (Feature name) for each of these 53 loci. Locus number is an arbitrary locus identifier that will be used to identify the loci in the following examples. Of the 53 features, 48 represent sequence within 1 kb of at least one annotated transcribed gene. These are referred to in the table by the Ensembl gene ID, as well as the official gene symbol for each gene (Gene Name). The genomic region in which a given microarray feature can report DNA methylation status is dependent upon the molecular size of the DNA fragments that were labeled for the microarray hybridizations. As described above, DNA in the size range of 1 to 4 kb was purified by agarose gel extraction and used as template for cyanogen dye labeling. Therefore, the genomic region interrogated by each microarray feature is at least 1 kb (i.e., 500 bp upstream and 500 bp downstream of the sequence represented by the microarray feature). Note that 5 features represent loci in which there is no annotated transcribed gene within this 1 kb “wingspan” (Locus numbers 3, 9, 20, 31, and 47). Also note that 8 features represent loci in which more than one annotated transcribed gene falls within wingspan (Locus numbers 21, 26, 27, 29, 35, 38, 39, and 53). DNA methylation at these loci can affect the regulation of any of these neighboring genes, and thus detection of gene expression from neighboring genes is also useful for determining the presence or absence of cancer for numerous types of diagnostic tests.

TABLE 1 Microarray Features Reporting Differential DNA Methylation And Identity Of Annotated Genes Within 1 kb Of Each Feature. Locus Featurename Number Ensembl Gene ID Gene Name ha1g_00681 1 ENSG00000105997 HOXA3 ha1p_39189 2 ENSG00000121853 GHSR ha1g_00644 3 N/A ha1p_81674 4 ENSG00000174197 MGA ha1p_81149 5 ENSG00000122971 ACADS ha1p_83841 6 ENSG00000178187 ZNF454 ha1p_38705 7 ENSG00000163638 ADAMTS9 ha1p_40164 8 ENSG00000118855 MFSD1 ha1p_23178 9 N/A ha1p_46057 10 ENSG00000132640 BTBD3 ha1p_40959 11 ENSG00000111707 SUDS3 ha1p_104423 12 ENSG00000008441 NFIX ha1g_00847 13 ENSG00000116106 EPHA4 ha1p_08347 14 ENSG00000134802 SLC43A3 ha1g_02416 15 ENSG00000172238 ATOH1 ha1p_87540 16 ENSG00000159403 PREDICTED: similar to Complement C1r subcomponent precursor ha1p_110107 17 ENSG00000122254 HS3ST2 ha1p_89799 18 ENSG00000163739 CXCL1 ha1p_45173 19 ENSG00000120915 EPHX2 ha1p_80771 20 N/A ha1p_69407 21 ENSG00000180667 YOD1 ENSG00000198878 O9P1L8_HUMAN ha1p_05406 22 ENSG00000165556 CDX2 ha1p_80287 23 ENSG00000109113 RAB34 ha1g_02345 24 ENSG00000122592 HOXA7 ha1p_36172 25 ENSG00000185070 FLRT2 ha1p_70459 26 ENSG00000163481 RNF25 ENSG00000163482 STK36 ha1p_105937 27 ENSG00000161551 ZNF577 ENSG00000198093 ZNF649 ha1p_89099 28 ENSG00000062485 CS ha1g_03099 29 ENSG00000066032 CTNNA2 ENSG00000162951 LRRTM1 ha1p_67625 30 ENSG00000130711 PRDM12 ha1g_00218 31 N/A ha1p_12535 32 ENSG00000149090 RAMP ha1p_105474 33 ENSG00000033627 ATP6V0A1 ha1p_74707 34 ENSG00000010278 CD9 ha1p_93325 35 ENSG00000101019 C20 orf44 ENSG00000125965 GDF5 ha1p_101161 36 ENSG00000130176 CNN1 ha1p_101251 37 ENSG00000142235 LMTK3 ha1p_69214 38 ENSG00000158927 C8 orf58 ENSG00000158941 KIAA1967 ENSG00000183646 ha1p_88517 39 ENSG00000101412 E2F1 ENSG00000125967 APBA2BP ha1p_103824 40 ENSG00000167178 ISLR2 ha1p_108445 41 ENSG00000175287 PHYHD1 ha1g_02210 42 ENSG00000151615 POU4F2 ha1p_103872 43 ENSG00000129009 ISLR ha1p_56412 44 ENSG00000175182 C3 orf40 ha1p_18292 45 ENSG00000115561 VPS24 ha1p_12075 46 ENSG00000164619 BMPER ha1p_22519 47 N/A ha1p_29531 48 ENSG00000060718 COL11A1 ha1p_58853 49 ENSG00000113648 H2AFY ha1p_35052 50 ENSG00000111341 MGP ha1p_67002 51 ENSG00000159445 THEM4 ha1p_45580 52 ENSG00000168079 SCARA5 ha1p_12646 53 ENSG00000107833 NPM3 ENSG00000198408 MGEA5

Example 2 Design of Independent DNA Methylation Verification and Validation Assays

PCR primers that interrogated the 53 loci predicted to be differentially methylated between breast tumor and adjacent histologically normal breast tissue were designed. Due to the functional properties of the enzyme, DNA methylation-dependent depletion of DNA fragments by McrBC is capable of monitoring the DNA methylation status of sequences neighboring the genomic sequences represented by the features on the microarray described in Example 1 (wingspan). Since the size of DNA fragments analyzed as described in Example 1 was approximately 1-4 kb, we selected a 1 kb region spanning the sequence represented by the microarray feature as an estimate of the predicted region of differential methylation. For each locus, PCR primers were selected within this approximately 1 kb region flanking the genomic sequence represented on the DNA microarray (approximately 500 bp upstream and 500 bp downstream). Selection of primer sequences was guided by uniqueness of the primer sequence across the genome, as well as the distribution of purine-CG sequences within the 1 kb region. PCR primer pairs were selected to amplify an approximately 400-600 bp sequence within each 1 kb region. For demonstration, an example of one such PCR amplicon design is shown in FIG. 1. A graphical representation of the transcription start site and 5′ structure of one predicted differentially methylated gene is indicated (A). The bar graph (B) indicates the relative local density of purine-CG sequences within this region. The relative position of the DNA microarray feature that reported differential DNA methylation at this locus is indicated by (C). PCR primers were selected to amplify the region indicated by (D). The vertical bars (E and F) represent the microarray DNA methylation measurement representing all breast tumors (E) and all normal breast samples (F). For example, this locus is predicted to be hypermethylated in the breast tumors (positive value) relative to the adjacent normal breast samples (negative value). Suitable PCR cycling conditions for the 53 primer pairs were empirically determined, and amplification of a specific PCR amplicon of the correct size was verified. The sequences of the 53 microarray features, primer pairs and amplicons are indicated in Table 2, and in the “SEQUENCE LISTING” section.

TABLE 2 Sequence identification numbers for all sequences described in the application. Locus Feature Feature Left Right Annealing Amplicon DNA Selection Number Name Seq. Primer Primer Temp. Seq. Region Seq. Criteria 1 ha1g_00681 1 54 107 66 C. 160 213 CG 2 ha1p_39189 2 55 108 66 C. 161 214 CG 3 ha1g_00644 3 56 109 66 C. 162 215 CG 4 ha1p_81674 4 57 110 66 C. 163 216 CG 5 ha1p_81149 5 58 111 66 C. 164 217 CG 6 ha1p_83841 6 59 112 66 C. 165 218 CG 7 ha1p_38705 7 60 113 66 C. 166 219 CG 8 ha1p_40164 8 61 114 66 C. 167 220 CG 9 ha1p_23178 9 62 115 66 C. 168 221 CG 10 ha1p_46057 10 63 116 66 C. 169 222 CG 11 ha1p_40959 11 64 117 66 C. 170 223 CG 12 ha1p_104423 12 65 118 66 C. 171 224 CG 13 ha1g_00847 13 66 119 66 C. 172 225 CG 14 ha1p_08347 14 67 120 66 C. 173 226 CG 15 ha1g_02416 15 68 121 66 C. 174 227 CG 16 ha1p_87540 16 69 122 66 C. 175 228 CG 17 ha1p_110107 17 70 123 66 C. 176 229 CG 18 ha1p_89799 18 71 124 66 C. 177 230 CG 19 ha1p_45173 19 72 125 66 C. 178 231 CG 20 ha1p_80771 20 73 126 66 C. 179 232 CG 21 ha1p_69407 21 74 127 66 C. 180 233 CG 22 ha1p_05406 22 75 128 66 C. 181 234 CG 23 ha1p_80287 23 76 129 66 C. 182 235 CG 24 ha1g_02345 24 77 130 72 C. 183 236 CG 25 ha1p_36172 25 78 131 72 C. 184 237 CG 26 ha1p_70459 26 79 132 66 C. 185 238 CG 27 ha1p_105937 27 80 133 66 C. 186 239 CG 28 ha1p_89099 28 81 134 66 C. 187 240 CG 29 ha1g_03099 29 82 135 66 C. 188 241 CG 30 ha1p_67625 30 83 136 72 C. 189 242 CG 31 ha1g_00218 31 84 137 66 C. 190 243 CG 32 ha1p_12535 32 85 138 66 C. 191 244 CG 33 ha1p_105474 33 86 139 66 C. 192 245 1 kb 34 ha1p_74707 34 87 140 66 C. 193 246 CG 35 ha1p_93325 35 88 141 66 C. 194 247 1 kb 36 ha1p_101161 36 89 142 66 C. 195 248 1 kb 37 ha1p_101251 37 90 143 66 C. 196 249 1 kb 38 ha1p_69214 38 91 144 66 C. 197 250 1 kb 39 ha1p_88517 39 92 145 66 C. 198 251 CG 40 ha1p_103824 40 93 146 66 C. 199 252 CG 41 ha1p_108445 41 94 147 72 C. 200 253 1 kb 42 ha1g_02210 42 95 148 72 C. 201 254 CG 43 ha1p_103872 43 96 149 66 C. 202 255 CG 44 ha1p_56412 44 97 150 72 C. 203 256 CG 45 ha1p_18292 45 98 151 72 C. 204 257 CG 46 ha1p_12075 46 99 152 66 C. 205 258 CG 47 ha1p_22519 47 100 153 66 C. 206 259 CG 48 ha1p_29531 48 101 154 66 C. 207 260 CG 49 ha1p_58853 49 102 155 66 C. 208 261 CG 50 ha1p_35052 50 103 156 66 C. 209 262 1 kb 51 ha1p_67002 51 104 157 72 C. 210 263 CG 52 ha1p_45580 52 105 158 72 C. 211 264 CG 53 ha1p_12646 53 106 159 66 C. 212 265 CG See, section “SEQUENCE LISTING” for actual sequences as listed by locus number in the table.

Example 3 Verification of Microarray DNA Methylation Predictions

Initially, the DNA methylation state of these 53 loci was independently assayed in the 10 infiltrating ductal breast carcinoma samples and the 10 matched adjacent histologically normal samples described above (i.e., the discovery tissue panel used for microarray experiments). DNA methylation was assayed by a quantitative PCR approach utilizing digestion by the McrBC restriction enzyme to monitor DNA methylation status. Genomic DNA purified from each sample was split into two equal portions of 9.6 μg. One 9.6 μg portion (Treated Portion) was digested with McrBC in a total volume of 120 μL including 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New. England Biolabs), 2 mM GTP (Roche) and 80 units of McrBC enzyme (New England Biolabs). The second 9.6 μg portion (Untreated Portion) was treated exactly the same as the Treated Portion, except that 8 μL of sterile 50% glycerol was added instead of McrBC enzyme. Reactions were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. for 20 minutes to inactivate McrBC.

The extent of McrBC cleavage at each locus was monitored by quantitative real-time PCR (qPCR). For each assayed locus, qPCR was performed using 20 ng of the Untreated Portion DNA as template and, separately, using 20 ng of the Treated Portion DNA as template. Each reaction was performed in 10 μL total volume including 1× LightCycler 480 SYBR Green I Master mix (Roche) and 625 nM of each primer. Reactions were run in a Roche LightCycler 480 instrument. Optimal annealing temperatures varied depending on the primer pair. Primer sequences (Left Primer; Right Primer) and appropriate annealing temperatures (Annealing Temp.) are shown in Table 2. Cycling conditions were: 95° C. for 5 min.; 45 cycles of 95° C. for 1 min., [annealing temperature, see Table 2] for 30 sec., 72° C. for 1 min., 83° C. for 2 sec. followed by a plate read. Melting curves were calculated under the following conditions: 95° C. for 5 sec., 65° C. for 1 min., 65° C. to 95° C. at 2.5° C./sec. ramp rate with continuous plate reads. Each Untreated/Treated qPCR reaction pair was performed in duplicate. The difference in the cycle number at which amplification crossed threshold (delta Ct) was calculated for each Untreated/Treated qPCR reaction pair by subtracting the Ct of the Untreated Portion from the Ct of the Treated Portion. Because McrBC-mediated cleavage between the two primers increases the Ct of the Treated Portion, increasing delta Ct values reflect increasing measurements of local DNA methylation densities. The average delta Ct between the two replicate Untreated/Treated qPCR reactions was calculated, as well as the standard deviation between the two delta Ct values.

For demonstration purposes, amplification profiles for one locus (GHSR) in a tumor sample and a normal sample are shown in FIG. 2. Panel A shows the untreated/treated PCR replicate 1 for amplification of the GHSR amplicon in a breast tumor sample. The delta Ct (Treated 1—Untreated 1) is 5.38 cycles. Panel B shows the untreated/treated PCR replicate 2 for amplification of the same amplicon from the same tumor sample. The delta Ct (Treated 2—Untreated 2) is 5.40 cycles. The average delta Ct of the two replicates is 5.39 cycles, representing a ˜97% reduction of amplifiable copies in the treated relative to the untreated portions [100%-((1/2̂delta Ct)×100)]. The standard deviation of the delta Ct's between the two qPCR replicates is 0.01 cycles. Panel C shows the untreated/treated PCR replicate 1 for amplification of the GHSR amplicon in a normal breast sample. The delta Ct (Treated 1—Untreated 1) is 0.18 cycles. Panel D shows the untreated/treated PCR replicate 2 for amplification of the same amplicon from the same normal sample. The delta Ct (Treated 2—Untreated 2) is 0.03 cycles. The average delta Ct of the two replicates is 0.11 cycles, representing a ˜7% reduction of amplifiable copies in the treated relative to the untreated portions. The standard deviation of the delta Ct's between the two qPCR replicates is 0.11 cycles. The average delta Ct of the tumor sample would be scored as a methylated locus. In contrast, the average delta Ct of the normal sample would be scored as a relatively unmethylated locus. An average delta Ct of >1.0 cycle, representing >˜50% reduction of amplifiable copies in the treated relative to the untreated portions, was set as the threshold for scoring a sample as positive for DNA methylation. Any average delta Ct measurement with a standard deviation >1.0 cycle in qPCR replicates was excluded as an unreliable measurement (ND in FIG. 3). Finally, any average delta Ct<0 was adjusted to 0.

FIG. 3 shows the results of the DNA methylation measurements for the 53 loci in the 10 tumor samples and 10 normal samples used in the microarray discovery experiment. Open boxes represent loci that are unmethylated (average delta Ct<1.0), grey boxes represent loci that are methylated (average delta Ct>1 and <2), and black boxes represent loci that are densely methylated (average delta Ct>2).

Example 4 Validation of DNA Methylation Changes in Independent Breast Tumor and Normal Breast Samples

The differential DNA methylation status of the 53 loci was further validated by analyzing an independent panel of 16 infiltrating ductal breast carcinoma samples (1 Stage I, 4 Stage II, 11Stage III) and 25 normal breast tissue samples. The normal breast tissues included in this panel were obtained from biopsies unrelated to breast cancer. Each sample was split into two equal portions of 4 μg. One portion was digested with McrBC (Treated Portion) in a total volume of 200 μL including 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 32 units McrBC (New England Biolabs). The second portion was mock treated under identical conditions, except that 3.2 μL sterile 50% glycerol was added instead of McrBC enzyme (Untreated Portion). Samples were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. to inactivate the McrBC enzyme. qPCR reactions and data analysis were performed as described in Example 3.

The DNA methylation state measurements are summarized in FIG. 4. As described above, each locus was scored as unmethylated (average delta Ct<1.0, open boxes), methylated (average delta Ct>1.0 and <2.0, grey boxes) or densely methylated (average delta Ct>2.0, black boxes). Measurements with a standard deviation between pPCR replicates >1 cycle were not included in the analysis (ND). Table 3 indicates the percent sensitivity and specificity for each locus. Sensitivity reflects the frequency of scoring a known tumor sample as positive for DNA methylation at each locus. Specificity reflects the frequency of scoring a known normal sample as negative for DNA methylation at each locus. As described above, an average delta Ct>1.0 (Treated Portion—Untreated Portion) was used as a threshold to score a sample as positive for DNA methylation at each locus (representing >˜50% depletion of amplifiable molecules in the DNA methylation-dependent restricted population relative to the untreated population). Percent sensitivity was calculated as the number of tumor samples with an average delta Ct>1.0 divided by the total number of tumor samples analyzed for that locus (i.e. excluding any measurements with a standard deviation between qPCR replicates >1 cycle)×100. Percent specificity was calculated as (1—(the number of normal samples with an average delta Ct>1.0 divided by the total number of normal samples analyzed for that locus))×100. As shown in Table 3, the 53 loci have sensitivities >13% and specificities >80%. Notably, 33 of the 53 loci have 100% specificity. It is important to point out that the sensitivity and specificity of the differential DNA methylation status of any given locus may be increased by further optimization of the precise local genetic region interrogated by a DNA methylation-sensing assay.

TABLE 3A Sensitivity and specificity of differentially methylated loci in a panel of 25 normal breast and 16 breast tumor samples. FEATURE LOCUS ID NUMBER SENSITIVITY SPECIFICITY ha1g_00681 1 86% 100% ha1p_39189 2 81% 100% ha1g_00644 3 79% 100% ha1p_81674 4 69% 100% ha1p_81149 5 69% 100% ha1p_83841 6 69% 100% ha1p_38705 7 63% 100% ha1p_40164 8 63% 100% ha1p_23178 9 63% 100% ha1p_46057 10 56% 100% ha1p_40959 11 56% 100% ha1p_104423 12 50% 100% ha1g_00847 13 50% 100% ha1p_08347 14 50% 100% ha1g_02416 15 44% 100% ha1p_87540 16 44% 100% ha1p_110107 17 38% 100% ha1p_89799 18 31% 100% ha1p_45173 19 31% 100% ha1p_80771 20 25% 100% ha1p_69407 21 25% 100% ha1p_05406 22 25% 100% ha1p_80287 23 25% 100% ha1g_02345 24 20% 100% ha1p_36172 25 20% 100% hal p_70459 26 19% 100% ha1p_105937 27 19% 100% ha1p_89099 28 19% 100% ha1g_03099 29 14% 100% ha1p_67625 30 13% 100% ha1g_00218 31 13% 100% ha1p_12535 32 75%  96% ha1p_105474 33 75%  96% ha1p_74707 34 69%  96% ha1p_93325 35 69%  96% ha1p_101161 36 56%  96% ha1p_101251 37 87%  96% ha1p_69214 38 81%  96% ha1p_88517 39 69%  96% ha1p_103824 40 17%  96% ha1p_108445 41 60%  95% ha1g_02210 42 18%  95% ha1p_103872 43 94%  92% ha1p_56412 44 79%  92% ha1p_18292 45 60%  91% ha1p_12075 46 31%  90% ha1p_22519 47 88%  88% ha1p_29531 48 88%  88% ha1p_58853 49 50%  88% ha1p_35052 50 31%  88% ha1p_67002 51 64%  83% ha1p_45580 52 40%  83% ha1p_12646 53 81%  80%

Example 5 Further Validation of Selected DNA Methylation Biomarkers in a Larger Panel of Breast Tumor Samples, Normal Breast Samples, and Normal Female Peripheral Blood Samples

A panel of 15 loci were selected for further validation in a panel of 9 additional infiltrating ductal breast carcinoma samples, bringing the total number of tumor samples analyzed to 25 (1 Stage II, 8 Stage III). In addition, 25 normal female peripheral blood samples were analyzed. Samples were treated and analyzed as described in Example 4. FIG. 5 shows the results of these analyses, including the 25 normal breast samples described in Example 4. As shown in Table 4, these loci display >17% sensitivity, >92% specificity relative to normal breast tissue, and >92% specificity relative to normal female peripheral blood.

TABLE 4 Sensitivity and specificity of differentially methylated loci in a panel of 25 normal breast and 25 breast tumor samples and 25 normal blood samples. SPECIFICITY FEATURE LOCUS SENSI- VS NORMAL SPECIFICITY ID NUMBER TIVITY BREAST VS BLOOD ha1p_39189 2 84% 100%  96% ha1g_00644 3 83% 100% 100% ha1p_81674 4 76% 100%  95% ha1p_74707 34 72%  96% 100% ha1p_101251 37 88%  96%  92% ha1g_02416 15 54% 100% 100% ha1p_110107 17 52% 100% 100% ha1p_89799 18 40% 100% 100% ha1p_80771 20 36% 100% 100% ha1p_69407 21 26%  92%  96% ha1p_05406 22 32% 100% 100% ha1g_02345 24 17% 100% 100% ha1p_36172 25 38% 100% 100% ha1p_70459 26 24% 100%  96% ha1p_67625 30 35% 100% 100%

Example 6 Demonstration of a DNA Methylation Measurement Threshold

In the examples above, a threshold for scoring differential methylation (average delta Ct>1.0) was established and indiscriminately applied to all loci. However, the most informative threshold is dependent upon the specific locus in question. This is demonstrated in FIG. 6. The graph shows the average delta Ct (Treated Portion—Untreated Portion) for the analyzed region of the GHSR locus in 25 tumor samples, 25 normal breast samples, and 24 normal female peripheral blood samples. Using an average delta Ct threshold of >1.0 as the criteria for a positive DNA methylation measurement, sensitivity is 84%, specificity relative to normal tissue is 100% and specificity relative to blood is 96% (Table 4). However, an optimal threshold may be set for each individual locus, and this threshold is dependent upon the technology used to detect the differential DNA methylation state. For example, in the GSHR example shown in FIG. 6, a threshold of >1.3 (hatched line in figure) would adjust the specificity relative to blood to 100%.

Example 7 Validation of Selected DNA Methylation Biomarkers in a Panel Including Approximately 100 Breast Tumor Samples and 100 Normal Breast Samples

A panel of 16 biomarker loci was further validated in additional breast tumor and normal breast samples. In total, approximately 100 samples were analyzed for each group. The total number of samples analyzed for each biomarker and for each sample category is reported in Table 5.

TABLE 5 Sensitivity and specificity of differentially methylated loci in a panel of approximately 100 breast tumor samples, 100 normal breast samples and 25 normal blood samples. No. % No. % No. Positive Total Specficity Positive Total Specficity Locus Positive Total % Normal Normal (Normal Normal Normal (Normal Feature ID Number Tumor Tumor Sensitivity Breast Breast Breast) Blood Blood Blood) ha1p_39189 2 87 102 85% 1 103  99% 1 24  96% ha1g_00644 3 74 101 73% 1 104  99% 0 25 100% ha1p_81674 4 71 99 72% 10 93  89% 1 19  95% ha1p_101251 37 67 101 66% 4 102  96% 2 24  92% ha1p_74707 34 55 102 54% 1 104  99% 0 25 100% ha1g_02416 15 41 101 41% 1 104  99% 0 25 100% ha1p_67625 30 36 91 40% 1 99  99% 0 21 100% ha1p_69407 21 37 100 37% 6 103  94% 1 23  96% ha1p_104423 12 36 98 37% 0 97 100% 1 24  96% ha1p_36172 25 34 102 33% 0 104 100% 0 23 100% ha1p_89799 18 27 101 27% 0 99 100% 0 24 100% ha1p_80771 20 27 103 26% 0 104 100% 0 25 100% ha1p_70459 26 26 101 26% 0 103 100% 1 24  96% ha1p_110107 17 24 101 24% 0 104 100% 0 24 100% ha1p_05406 22 23 103 22% 0 103 100% 0 24 100% ha1g_02345 24 17 102 17% 0 103 100% 0 22 100% No. Positive Tumor: Number of tumor samples that reported avg. dCt ≧ 1.0 (methylated locus). Total Tumor: Number of tumor samples tested. % Sensitivity: (No. Positive Tumor/Total Tumor) × 100 No. Positive Normal Breast: Number of normal breast samples that reported avg. dCt ≧ 1.0 (methylated locus). Total Normal Breast: Number of normal breast samples tested. % Specificity (Nomral Breast): (1 − (No. Positive Nomral Breast/Total Normal Breast)) × 100 No. Posivite Normal Blood: Number of normal blood samples that reported avg. dCt ≧ 1.0 (methylated locus). Total Normal Blood: Number of normal blood samples tested. % Specificity (Normal Blood): (1 − (No. Positive Normal Blood/Total Normal Blood)) × 100

Example 8 Bisulfite Sequencing Confirmation of Differential DNA Methylation

An example of confirmation of differential DNA methylation by bisulfite sequencing is shown in FIG. 7. Primers were designed to amplify a 130 bp amplicon within the 412 bp region of Nuclear Factor 1X-type analyzed by qPCR (as discussed in the Examples above) from bisulfite converted genomic DNA. Primers sequences lack CpG dinucleotides, and therefore amplify bisulfite converted DNA independently of DNA methylation status. Products were amplified from one tumor sample (positive for DNA methylation) and from one pooled normal female peripheral blood sample. Amplicons were purified and cloned using TA cloning kits (Invitrogen). Eighteen (18) independent clones were sequenced for the tumor sample. Seven (7) independent clones were sequenced for the blood sample. Bisulfite treatment results in conversion of unmethylated cytosines to uracil, but does not convert methylated cytosines. The percent methylation of each CpG dinucleotide within the region was calculated as the number of sequence reads of C at each CpG divided by the total number of sequence reads. FIG. 7A shows the % methylation occupancy for each of the 18 CpG dinucleotides in the tumor sample. FIG. 7B shows the % methylation occupancy for each of the 18 CpG dinucleotides in the normal blood sample. All 18 CpG dinucleotides are methylated in the tumor (occupany ranging from 11% to 89%). However, only one CpG dinucleotide displayed methylation in the normal blood sample (14%).

To provide further confirmation of DNA methylation differences and to justify the qPCR based strategy for high-throughput detection of DNA methylation, three loci were analyzed by bisulfite genomic sequencing. Primers were designed to amplify approximately 150 bp amplicons within the region of three loci that were analyzed by qPCR as described above. The loci included feature ID ha1p_(—)39189 (locus number 2), ha1g_(—)00644 (locus number 3) and ha1p_(—)104423 (locus number 12). Primer sequences lacked CpG dinucleotides, and therefore amplify bisulfite converted DNA independently of DNA methylation status. For each amplicon, products were amplified from three normal breast DNA samples that reported average dCt values <0.5, three normal breast DNA samples that reported average dCt values between 0.5 and 1.0, and three breast tumor DNA samples that reported average dCt values greater than 1.0. Amplicons were purified and cloned using TA cloning kits (Invitrogen). At least 29 independent clones were sequenced per amplicon, per locus. FIG. 8 shows the median 5-methylcytosine content for all sequenced clones per amplicon plotted against the average dCt value for that locus in the same DNA sample. The dashed vertical line represents the dCt=1.0 threshold used to indicate a positive qPCR measurement for DNA methylation detection. These data verify the differential DNA methylation content in tumors relative to normal breast samples. Furthermore, the linear relationship between the qPCR measurement and the 5-methylcytosine content determined by bisulfate sequencing (R²=0.7965) provides justification for the high-throughput qPCR method for DNA methylation detection.

Example 9 Selection of Sequence Identified as Potential Region of Differential DNA Methylation

As described in the examples above, the loci identified as differentially methylated were originally discovered based on DNA methylation-dependent microarray analyses. The sequences of the 53 microarray features reporting this differential methylation are indicated in Table 2 and in the “SEQUENCE LISTING” section. Because the “wingspan” of genomic interrogation by each feature is conservatively 1 kb, PCR primers that amplify an amplicon within a 1 kb region surrounding the sequence represented by each microarray feature were selected and used for independent verification and validation experiments. Primer sequences and amplicon sequences are indicated in Table 2 and in the “SEQUENCE LISTING” section. To optimize successful PCR amplification, these amplicons were designed to be less than the entire 1 kb region represented by the wingspan of the microarray feature. However, it should be noted that differential methylation may be detectable anywhere within this sequence window. For each locus, the sequence representing at least this 1 kb region flanking the sequence represented by the microarray feature was selected as the claimed potentially differentially methylated genomic region. These sequences are indicated in Table 2 (DNA Region Sequences) and in the “SEQUENCE LISTING” section. Sequences claimed based on the 1 Kb region flanking the sequence represented by the microarray feature are indicated by “1 kb” in Table 2 (Selection Criteria).

In addition, the local CpG density surrounding each region was calculated. Approximately 10 kb of sequence both upstream and downstream of each feature was extracted from the human genome. For each 20 kb region of the genome, a sliding window of 500 bp moving in 100 bp steps was used to calculate the CG density. CG density was expressed as the ratio of CG dinucleotides per kb. An example is shown in FIG. 9 and illustrates the position of the transcription start site of the GHSR gene relative to the regional CpG density of the surrounding sequence. In this example, methylation anywhere with the ˜4 kb peak of CpG density associated with the promoter region of the gene is monitored and is useful in a clinical diagnostic assay. Loci in which the claimed region was determined by analysis of local CpG density are indicated by “CG” in Table 2 (Selection Criteria). As diagrammed in FIG. 9, the claimed sequences were selected based on setting the local minimum of CpG density flanking the sequence represented by the PCR amplicon as the upstream and downstream boundaries.

Example 10 Demonstration that Differential DNA Methylation is Detectable in Early Stage Disease

Although fewer Stage I tumors compared to Stage 11 or III tumors were analyzed (8 of 103 samples), the inclusion of a small number of Stage I tumors allowed a determination of whether the differential methylation events are related to tumor stage. FIG. 10A shows a plot of the frequency of hypermethylation of the 16 loci in the 8 Stage I tumors (i.e. the percentage of Stage I tumors scoring as intermediately to densely methylated) versus the Stage II and III tumors. The relationship between the two sensitivity calculations (R²=0.887; slope=0.9815) indicates that the frequency of hypermethylation of these loci is similar regardless of tumor stage. Therefore, for the majority of loci, the differential methylation events are just as likely to be present in a Stage I tumor as they are in later stage tumors. The proportion of methylated loci in tumors at each stage was then analyzed for three selected loci. The percent depletion by McrBC for each sample in which a given locus scored as methylated was calculated [1-(1/2̂delta Ct (McrBC digested−Mock treated))* 100] to provide a measure of the load of methylated molecules within the sample. The mean percent depletion at each tumor stage is shown in FIG. 10B. While there is a trend for increased methylation density at these loci with increasing tumor stage, methylation density of Stage I tumors is not significantly different than Stage II-III tumors, yet is dramatically different than the average of all normal samples. Therefore, differential methylation of these loci is independent of tumor stage in regards to both the frequency and the density of hypermethylation.

Example 11 Receiver-Operator Curve Analysis of Biomarker Sensitivity and Specificity

Receiver-operator characteristic (ROC) analyses were performed for each of the 16 loci described in Table 5 to determine optimal thresholds for calculation of sensitivity and specificity of the differential DNA methylation event. Examples of the primary qPCR data for four selected loci are shown in FIG. 11A. These plots demonstrate the overall discrimination between tumor, normal breast tissue and normal peripheral blood samples. The frequency at which tumor tissues were scored as differentially methylated at these loci was not significantly associated with either age of the cancer patient or estrogen receptor status of the patient's primary tumor. ROC curves for the corresponding four datasets are shown in FIG. 11B. Optimal thresholds were identified as the maximum sum of sensitivity and specificity calculated at each observed delta Ct value. The minimum allowed threshold was set at 0.5 so that calculations could not be based on thresholds within the variability range of the qPCR platform. Results are summarized in Table 6. Sensitivity and specificity calculations based on optimal thresholds are similar to those calculated using a standard delta Ct threshold of 1.0. As hypothesized, the direct global profiling of DNA methylation identified numerous novel DNA methylation-based biomarkers that display substantially improved sensitivity and specificity relative to the vast majority of previously identified differentially methylated genes in breast cancer. In fact, a single differentially methylated biomarker, located in the promoter region of GHSR, was capable of distinguishing IDC from normal and benign breast tissue with sensitivity of 90% and specificity of 96%. Other biomarkers displayed similar specificity, with decreasing sensitivity. Several of these biomarkers were hypermethylated at a higher frequency than the majority of previously reported hypermethylated biomarkers (i.e. 12 of 16 displayed sensitivity between 53% and 90%).

TABLE 6 Breast Cancer Biomarker Validation. BREAST TUMOR VS. Locus BREAST TUMOR VS. NORMAL BREAST NORMAL BLOOD Feature ID Number Sensitivity Pos. of Total Specificity Neg. of Total Threshold Specificity Neg. of Total Threshold ha1p_39189 2 90% 92 of 102 96%  99 of 103 0.64 100% 24 of 24 1.22 ha1g_00644 3 89% 90 of 101 92%  96 of 104 0.555 100% 25 of 25 0.695 ha1p_101251 37 77% 78 of 101 87%  89 of 102 0.755  96% 23 of 24 1.06 ha1p_81674 4 70% 69 of 99  92% 86 of 93 1.11  95% 18 of 19 0.935 ha1p_74707 34 69% 69 of 100 82%  84 of 103 0.615  87% 20 of 23 0.63 ha1g_02416 15 65% 66 of 102 97% 101 of 104 0.705 100% 25 of 25 0.535 ha1p_70459 26 63% 64 of 101 97% 101 of 104 0.525 100% 25 of 25 0.57 ha1p_69407 21 63% 64 of 101 93%  96 of 103 0.5  71% 17 of 24 0.55 ha1p_110107 17 60% 61 of 101 98% 102 of 104 0.51  96% 23 of 24 0.51 ha1p_36172 25 58% 59 of 102 100%  104 of 104 0.515 100% 23 of 23 0.515 ha1p_67625 30 56% 51 of 91  97% 96 of 99 0.545  95% 20 of 21 0.545 ha1p_104423 12 53% 52 of 98  97% 94 of 97 0.61  96% 23 of 24 0.855 ha1p_05406 22 48% 49 of 103 97% 100 of 103 0.51 100% 24 of 24 0.51 ha1p_89799 18 42% 42 of 101 99% 98 of 99 0.545 100% 24 of 24 0.71 ha1p_80771 20 38% 39 of 103 97% 101 of 104 0.5 100% 25 of 25 0.72 ha1g_02345 24 34% 35 of 102 97% 100 of 103 0.535 100% 22 of 22 0.535 Thresholds indicate the optimal average dCt value for distinction between tumor and non-tumor tissues.

Example 12 In-Depth Analysis of DNA Methylation by Bisulfite Sequencing

To provide an in-depth analysis of DNA methylation states relative to the qPCR-based measurements of methylated DNA load between different tissue types, we selected four loci (Locus Number 2, 3, 4 and 12) for extensive bisulfite sequencing analysis (FIG. 12). For each locus, analyzed regions overlapped those amplified in the qPCR assay. Primer pairs were designed to flank, but not include CpG dinucleotides. For analysis of each locus, we selected tumor samples that scored as intermediately to densely methylated and normal breast samples that scored as sparsely methylated. In addition, we selected three histology normal tumor-adjacent tissue samples. Loci were amplified from bisulfite-modified genomic DNA with primers that included patient-specific sequence tags to identify the tissue sample, and amplicons were pooled and sequenced. The average number of molecules analyzed for each locus in each sample was 587. To provide a general measurement of local DNA methylation density at each locus, the total number of CpG sites sequenced as C (methylated) was divided by the total of number of CpG sites sequenced for each individual sample. This percent methylated CpG value was then plotted against the qPCR methylation measurement for the same tissue sample (FIGS. 12A, C, E, G). Methylation load values obtained by bisulfite sequencing and by qPCR displayed a strong correlation for Locus number 2, 12 and 3 (R²=0.76, 0.87 and 0.78, respectively). While tumor samples displayed higher DNA methylation load at Locus number 4 than normal breast and adjacent histology normal breast samples, the non-tumor tissues displayed higher baseline DNA methylation densities than at the other loci (FIG. 12E). Next, the average occurrence of DNA methylation per CpG site in each tissue type was calculated (FIGS. 12B, D, F, H). In general, tumor samples displayed higher variability in methylation per CpG site than non-tumor (i.e., normal) samples (indicated by higher standard deviations for the average percent methylated CpGs). At each locus, the DNA methylation pattern was significantly hypermethylated relative to non-tumor samples. Furthermore, analysis of DNA methylation per CpG site provided an explanation for the higher baseline DNA methylation densities detected at the Locus Number 4 (FIG. 12F). In non-tumor samples, methylation densities at the first three CpG dinucleotides of the analyzed region were greater than 50%, while methylation of the following four CpG dinucleotides fell to lower densities more consistent with the baseline levels of methylation at the other analyzed loci. Interestingly, tumor samples displayed the same general methylation density pattern, but with significantly higher methylation density per CpG across the entire analyzed region. Together, these results confirm the hypermethylated state of these loci in breast cancer and provide an extensive validation of the accuracy of the qPCR-based method used to screen for DNA methylation changes in this study.

Example 13 DNA Hypermethylation is Associated with Decreased Transcription

To address the association between hypermethylation and transcription repression, we performed RT-PCR analyses of Locus Numbers 2, 4 and 12 (FIG. 13). Four breast infiltrating ductal carcinoma samples (>90% neoplastic cellularity) were analyzed for both DNA methylation and transcription of the three genes. DNA methylation was analyzed using the qPCR-based assays described above. For gene expression analyses, RT-PCR was performed using gene-specific primer pairs designed to flank intronic sequences so that the contribution of contaminating genomic DNA could be excluded. Analysis of GAPDH expression was performed as an internal control. Serial dilutions of first-strand cDNA preparations from tumor samples and a normal breast tissue sample were used as templates for PCR. As shown in FIG. 13, expression of Locus Number 2 transcript (GHSR) was undetectable in all four tumor samples, while expression was detected at 1:10 dilution of the normal breast cDNA. Consistent with the high sensitivity of hypermethylation at the GHSR locus (90%), all tumor samples demonstrated intermediate to dense DNA methylation at this locus. Likewise, all tumor samples displayed reduced expression of Locus Number 12 (NFX1) relative to normal breast tissue. Expression was undetectable in three of four tumor samples, whereas expression was detected in one tumor using undiluted cDNA as template. In normal breast tissue, expression was detected at 1:10 dilution of the cDNA. Interestingly, the tumor sample in which NFX1 expression was detected was scored as sparsely methylated by the qPCR-based assay. Methylation of the analyzed region of Locus Number 4 (MGA) was detected in all four tumors. However, reduced expression of MGA relative to normal breast was demonstrated in two of the four tumor samples.

Example 14 Detection of Tumor-Specific DNA Methylation in Fine Needle Aspirate Specimens

A common procedure to biopsy suspect masses in the breast is to perform fine needle aspiratation (FNA) of the tissue. The procedure involves removal of a small amount of fluid and cellular material from the suspect mass using a fine gauge needle. In addition, random periareolar fine needle aspiration (RPFNA) can be used to sample breast tissue in asymptomatic women to assess the risk of breast cancer development. Both approaches typically involve a cytological based diagnosis. Therefore, applying molecular tests to specimens obtained by these approaches promises to offer significantly improved clinical sensitivity and specificity relative to the current practice. To assess the ability to detect breast tumor-specific DNA methylation of the claimed differentially methylated loci, eight loci with varying frequency of differential DNA methylation in primary breast tissue were analyzed in a panel of 7 FNA specimens taken from women with confirmed infiltrating ductal breast carcinoma. DNA methylation was measured as described in Example 3. These included Locus Number 1, 2, 3, 4, 12, 37, 38 and 43. In FIG. 14, the percent sensitivity for each locus as listed in Tables 3A and 3B (i.e. the percentage of tumors that report and average dCt >1.0) is plotted against the percentage of unmatched FNA samples that report and average dCt>1.0. The frequency of DNA methylation detection (i.e. samples that report an average dCt>1.0) is very similar regardless of whether primary tumor samples or unmatched FNA specimens from confirmed breast cancer patients were analyzed (R²=0.7415, slope=0.817). These results suggest that the DNA methylation biomarkers described herein can be detected in a sample type relevant to molecular diagnostics of breast cancer.

Example 15 Analysis of DNA Methylation in Various Cancer Types

To address the applicability of the claimed DNA methylation biomarkers to cancer types other than breast cancer, all 53 claimed biomarkers were analyzed in panels of lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal and prostate tumors. Adjacent histology normal tissues were analyzed as controls. In addition, melanoma tumors were analyzed, although no adjacent normal tissues were available. The number of samples analyzed for each cancer type is provided in Table 7. DNA methylation was measured as described in Example 3. For each locus and each cancer type, the sensitivity and specificity for discriminating between tumor and adjacent normal tissue are reported in Tables 8-20. For melanoma tumors (Table 20), only sensitivity (the frequency of DNA methylation detection (i.e. samples that report an average dCt≧1.0)) is reported due to the unavailability of adjacent normal tissues. For each locus, the optimal threshold for discriminating between tumor and adjacent normal tissue was calculated following ROC curve analyses as described in Example 11. These data demonstrate that particular biomarker loci are applicable to cancer types other than breast cancer.

TABLE 7 Number of Tumor and Adjacent Normal tissues tested for methylation of the 53 biomarker loci. Cancer Type Tumor Adjacent Normal Lung 10 10 Renal 10 10 Liver 9 9 Ovarian 8 8 Head and Neck 9 5 Thyroid 9 9 Bladder 9 9 Cervical 10 9 Colon 8 8 Endometrial 14 9 Esophageal 9 10 Prostate 9 9 Melanoma 7 0

TABLE 8 Sensitivity and Specificity of differentially methylated loci in lung tumors relative to adjacent histological normal lung tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_105474 33 1.98 80% 8 of 10 100%  10 of 10 ha1p_39189 2 1.17 70% 7 of 10 100%  10 of 10 ha1p_23178 9 3.015 70% 7 of 10 100%  10 of 10 ha1p_89099 28 1.38 70% 7 of 10 100%  10 of 10 ha1g_00644 3 1.445 60% 6 of 10 100%  10 of 10 ha1p_40164 8 1.96 60% 3 of 5  100%  5 of 5 ha1p_81149 5 2.37 50% 5 of 10 100%  10 of 10 ha1p_08347 14 1.67 50% 4 of 8  100%  7 of 7 ha1p_12075 46 2.33 43% 3 of 7  100%  7 of 7 ha1p_40959 11 2.435 40% 4 of 10 100%  10 of 10 ha1p_36172 25 0.835 40% 4 of 10 100%  10 of 10 ha1p_56412 44 3.86 40% 4 of 10 100%  9 of 9 ha1g_03099 29 0.635 30% 3 of 10 100%  10 of 10 ha1p_67625 30 0.965 30% 3 of 10 100%  9 of 9 ha1p_93325 35 2.93 30% 3 of 10 100%  10 of 10 ha1p_103824 40 0.765 30% 3 of 10 100%  10 of 10 ha1p_69407 21 2.77 29% 2 of 7  100%  6 of 6 ha1p_81674 4 2.165 25% 2 of 8  100%  7 of 7 ha1p_80771 20 1.05 20% 2 of 10 100%  9 of 9 ha1g_00218 31 0.71 10% 1 of 10 100%  10 of 10 ha1p_45173 19 1.66 100%  10 of 10  90%  9 of 10 ha1p_83841 6 1.54 90% 9 of 10 90%  9 of 10 ha1p_46057 10 0.9 90% 9 of 10 90%  9 of 10 ha1p_105937 27 1.375 80% 8 of 10 90%  9 of 10 ha1p_69214 38 3.735 78% 7 of 9  90%  9 of 10 ha1p_18292 45 2.06 60% 6 of 10 90%  9 of 10 ha1p_12535 32 2.05 50% 5 of 10 90%  9 of 10 ha1p_67002 51 2.025 50% 5 of 10 90%  9 of 10 ha1p_87540 16 0.98 80% 8 of 10 89% 8 of 9 ha1p_108445 41 1.9 40% 4 of 10 89% 8 of 9 ha1p_88517 39 1.38 50% 3 of 6  83% 5 of 6 ha1p_29531 48 1.01 90% 9 of 10 80%  8 of 10 ha1p_58853 49 1.315 80% 8 of 10 80%  8 of 10 ha1p_103872 43 2.96 70% 7 of 10 80%  8 of 10 ha1p_89799 18 0.5 63% 5 of 8  80%  8 of 10 ha1p_104423 12 0.83 40% 4 of 10 80%  8 of 10 ha1p_80287 23 0.82 40% 4 of 10 80%  8 of 10 ha1p_74707 34 0.76 78% 7 of 9  78% 7 of 9 ha1p_38705 7 0.62 63% 5 of 8  78% 7 of 9 ha1g_00681 1 1.965 56% 5 of 9  78% 7 of 9 ha1p_35052 50 1.6 70% 7 of 10 70%  7 of 10 ha1p_45580 52 3.1 70% 7 of 10 70%  7 of 10 ha1g_02345 24 0.51 67% 6 of 9  67% 6 of 9 ha1p_05406 22 0.615 71% 5 of 7  63% 5 of 8 ha1p_22519 47 1.6 100%  10 of 10  60%  6 of 10 ha1g_02416 15 0.58 90% 9 of 10 60%  6 of 10 ha1p_101161 36 0.885 80% 8 of 10 60%  6 of 10 ha1g_00847 13 0.53 63% 5 of 8  60%  6 of 10 ha1g_02210 42 0.605 60% 6 of 10 60%  6 of 10 ha1p_70459 26 0.54 100%  6 of 6  56% 5 of 9 ha1p_12646 53 5.455 78% 7 of 9  56% 5 of 9 ha1p_101251 37 1.34 100%  10 of 10  40%  4 of 10 ha1p_110107 17 0.55 86% 6 of 7  40%  4 of 10 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 9 Sensitivity and Specificity of differentially methylated loci in renal tumors relative to adjacent histological normal kidney tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_29531 48 0.68 80% 8 of 10 100%  10 of 10 ha1p_23178 9 1.52 70% 7 of 10 100%  10 of 10 ha1p_69407 21 1.255 70% 7 of 10 100%  10 of 10 ha1p_22519 47 1.68 67% 6 of 9  100%  10 of 10 ha1g_00644 3 1.135 60% 6 of 10 100%  10 of 10 ha1p_83841 6 1.2 60% 6 of 10 100%  9 of 9 ha1p_80771 20 0.535 60% 6 of 10 100%  10 of 10 ha1p_103872 43 3.25 60% 6 of 10 100%  10 of 10 ha1p_89099 28 0.935 56% 5 of 9  100%  9 of 9 ha1p_74707 34 1.6 56% 5 of 9  100%  10 of 10 ha1g_02416 15 0.56 50% 5 of 10 100%  9 of 9 ha1p_45173 19 0.675 50% 5 of 10 100%  10 of 10 ha1p_105937 27 0.99 50% 5 of 10 100%  10 of 10 ha1p_93325 35 1.155 50% 5 of 10 100%  10 of 10 ha1p_108445 41 1.025 50% 5 of 10 100%  10 of 10 ha1p_103824 40 0.82 44% 4 of 9  100%  10 of 10 ha1p_56412 44 1.84 44% 4 of 9  100%  10 of 10 ha1p_38705 7 1.28 40% 4 of 10 100%  9 of 9 ha1p_70459 26 0.74 40% 4 of 10 100%  9 of 9 ha1p_36172 25 0.92 33% 3 of 9  100%  10 of 10 ha1g_02345 24 0.565 30% 3 of 10 100%  10 of 10 ha1p_80287 23 1.135 22% 2 of 9  100%  10 of 10 ha1g_03099 29 0.555 20% 2 of 10 100%  10 of 10 ha1p_58853 49 1.195 11% 1 of 9  100%  10 of 10 ha1p_08347 14 2.65 100%  9 of 9  90%  9 of 10 ha1p_46057 10 1.325 80% 8 of 10 90%  9 of 10 ha1g_00681 1 1.905 60% 6 of 10 90%  9 of 10 ha1p_18292 45 1.09 60% 6 of 10 90%  9 of 10 ha1p_87540 16 0.925 40% 4 of 10 90%  9 of 10 ha1p_89799 18 0.605 40% 4 of 10 90%  9 of 10 ha1p_05406 22 0.52 40% 4 of 10 90%  9 of 10 ha1g_00218 31 0.505 20% 2 of 10 90%  9 of 10 ha1p_39189 2 1.03 80% 8 of 10 89% 8 of 9 ha1p_67625 30 0.79 60% 6 of 10 89% 8 of 9 ha1p_88517 39 1.98 50% 5 of 10 89% 8 of 9 ha1p_35052 50 1.495 100%  10 of 10  80%  8 of 10 ha1p_40164 8 0.825 90% 9 of 10 80%  8 of 10 ha1p_67002 51 1.565 90% 9 of 10 80%  8 of 10 ha1p_40959 11 0.88 80% 8 of 10 80%  8 of 10 ha1p_12535 32 0.93 70% 7 of 10 80%  8 of 10 ha1p_12646 53 4.23 60% 6 of 10 80%  8 of 10 ha1p_110107 17 0.53 56% 5 of 9  80%  8 of 10 ha1p_101161 36 0.93 40% 4 of 10 80%  8 of 10 ha1g_02210 42 0.52 33% 3 of 9  80%  8 of 10 ha1g_00847 13 0.7 56% 5 of 9  78% 7 of 9 ha1p_81674 4 1.365 89% 8 of 9  67% 6 of 9 ha1p_12075 46 1.66 89% 8 of 9  56% 5 of 9 ha1p_45580 52 2.1 100%  10 of 10  50%  5 of 10 ha1p_81149 5 0.875 90% 9 of 10 50%  5 of 10 ha1p_105474 33 1.035 70% 7 of 10 50%  5 of 10 ha1p_101251 37 1 70% 7 of 10 50%  5 of 10 ha1p_104423 12 0.68 80% 8 of 10 40%  4 of 10 ha1p_69214 38 1.085 100%  10 of 10  33% 3 of 9 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 10 Sensitivity and Specificity of differentially methylated loci in liver tumors relative to adjacent histological normal liver tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_89799 18 3.01 67% 6 of 9 100%  9 of 9 ha1p_81674 4 1.66 56% 5 of 9 100%  9 of 9 ha1p_56412 44 1.94 56% 5 of 9 100%  9 of 9 ha1p_67002 51 1.825 50% 4 of 8 100%  9 of 9 ha1p_81149 5 1.39 38% 3 of 8 100%  9 of 9 ha1p_83841 6 1.305 38% 3 of 8 100%  9 of 9 ha1p_74707 34 1.335 38% 3 of 8 100%  8 of 8 ha1p_80771 20 0.87 33% 3 of 9 100%  9 of 9 ha1p_70459 26 2.785 33% 3 of 9 100%  9 of 9 ha1p_89099 28 2.715 33% 3 of 9 100%  8 of 8 ha1p_12535 32 2.1 33% 3 of 9 100%  9 of 9 ha1p_39189 2 4.805 25% 2 of 8 100%  9 of 9 ha1g_00218 31 0.775 22% 2 of 9 100%  9 of 9 ha1p_12646 53 3.49 17% 1 of 6 100%  7 of 7 ha1g_00847 13 0.86 78% 7 of 9 89% 8 of 9 ha1p_05406 22 1.465 78% 7 of 9 89% 8 of 9 ha1p_105474 33 1.55 78% 7 of 9 89% 8 of 9 ha1p_69407 21 3.06 67% 6 of 9 89% 8 of 9 ha1g_02416 15 0.94 50% 4 of 8 89% 8 of 9 ha1p_40164 8 1.91 44% 4 of 9 89% 8 of 9 ha1p_101251 37 1.195 44% 4 of 9 89% 8 of 9 ha1p_110107 17 1.955 38% 3 of 8 89% 8 of 9 ha1p_105937 27 1.585 38% 3 of 8 89% 8 of 9 ha1p_18292 45 2.51 63% 5 of 8 88% 7 of 8 ha1p_35052 50 6 89% 8 of 9 78% 7 of 9 ha1p_67625 30 1.97 83% 5 of 6 78% 7 of 9 ha1p_23178 9 2.655 78% 7 of 9 78% 7 of 9 ha1p_93325 35 4.85 78% 7 of 9 78% 7 of 9 ha1g_00681 1 0.615 75% 6 of 8 78% 7 of 9 ha1p_22519 47 1.975 71% 5 of 7 78% 7 of 9 ha1p_38705 7 2.51 67% 6 of 9 78% 7 of 9 ha1p_08347 14 1.08 67% 6 of 9 78% 7 of 9 ha1p_101161 36 1.07 67% 6 of 9 78% 7 of 9 ha1p_69214 38 3.31 67% 6 of 9 78% 7 of 9 ha1g_02345 24 0.695 63% 5 of 8 78% 7 of 9 ha1p_87540 16 1.45 56% 5 of 9 78% 7 of 9 ha1p_45173 19 4.58 56% 5 of 9 78% 7 of 9 ha1p_88517 39 1.86 44% 4 of 9 78% 7 of 9 ha1g_00644 3 0.545 22% 2 of 9 78% 7 of 9 ha1p_103824 40 1.045 75% 6 of 8 75% 6 of 8 ha1p_108445 41 1.595 67% 4 of 6 75% 6 of 8 ha1p_45580 52 2.055 63% 5 of 8 75% 6 of 8 ha1p_80287 23 3.75 83% 5 of 6 71% 5 of 7 ha1p_46057 10 1.685 67% 6 of 9 67% 6 of 9 ha1p_36172 25 2.95 67% 6 of 9 67% 6 of 9 ha1g_02210 42 0.52 67% 6 of 9 67% 6 of 9 ha1p_12075 46 1.725 67% 6 of 9 67% 6 of 9 ha1p_29531 48 3.385 67% 6 of 9 67% 6 of 9 ha1p_58853 49 1.905 63% 5 of 8 67% 6 of 9 ha1p_103872 43 1.02 56% 5 of 9 67% 6 of 9 ha1g_03099 29 0.68 67% 6 of 9 56% 5 of 9 ha1p_104423 12 0.845 100%  7 of 7 43% 3 of 7 ha1p_40959 11 3.51 75% 6 of 8 33% 3 of 9 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 11 Sensitivity and Specificity of differentially methylated loci in ovarian tumors relative to adjacent histological normal ovary tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1g_00644 3 0.535 100%  7 of 7 100% 8 of 8 ha1p_46057 10 0.6 100%  8 of 8 100% 8 of 8 ha1p_81674 4 1.47 88% 7 of 8 100% 8 of 8 ha1p_81149 5 1.335 88% 7 of 8 100% 8 of 8 ha1g_00847 13 0.69 88% 7 of 8 100% 8 of 8 ha1p_12535 32 0.695 88% 7 of 8 100% 8 of 8 ha1p_69214 38 1.06 88% 7 of 8 100% 7 of 7 ha1p_22519 47 1.395 88% 7 of 8 100% 8 of 8 ha1p_12646 53 0.88 88% 7 of 8 100% 8 of 8 ha1p_38705 7 0.65 80% 4 of 5 100% 5 of 5 ha1g_00681 1 1.01 75% 6 of 8 100% 8 of 8 ha1p_39189 2 0.97 75% 6 of 8 100% 8 of 8 ha1p_83841 6 0.83 75% 6 of 8 100% 7 of 7 ha1p_23178 9 0.655 75% 6 of 8 100% 8 of 8 ha1p_08347 14 1.165 75% 6 of 8 100% 8 of 8 ha1p_45173 19 0.74 75% 6 of 8 100% 8 of 8 ha1p_105474 33 1.115 75% 6 of 8 100% 8 of 8 ha1p_101161 36 0.53 75% 6 of 8 100% 8 of 8 ha1p_103872 43 0.77 75% 6 of 8 100% 8 of 8 ha1p_104423 12 0.54 63% 5 of 8 100% 8 of 8 ha1g_02416 15 0.665 63% 5 of 8 100% 8 of 8 ha1p_05406 22 0.92 63% 5 of 8 100% 8 of 8 ha1p_89099 28 0.575 63% 5 of 8 100% 8 of 8 ha1p_103824 40 0.635 63% 5 of 8 100% 8 of 8 ha1p_12075 46 0.87 63% 5 of 8 100% 8 of 8 ha1p_18292 45 1.335 57% 4 of 7 100% 8 of 8 ha1p_110107 17 0.7 50% 3 of 6 100% 8 of 8 ha1p_67625 30 1.105 50% 4 of 8 100% 8 of 8 ha1p_93325 35 0.505 50% 4 of 8 100% 8 of 8 ha1p_56412 44 1.63 50% 4 of 8 100% 7 of 7 ha1p_58853 49 1.215 50% 4 of 8 100% 8 of 8 ha1p_45580 52 1.6 50% 4 of 8 100% 8 of 8 ha1p_87540 16 0.715 43% 3 of 7 100% 8 of 8 ha1p_89799 18 0.865 43% 3 of 7 100% 7 of 7 ha1p_36172 25 1.235 38% 3 of 8 100% 8 of 8 ha1p_105937 27 0.81 38% 3 of 8 100% 8 of 8 ha1g_03099 29 0.6 38% 3 of 8 100% 8 of 8 ha1p_74707 34 0.665 38% 3 of 8 100% 8 of 8 ha1p_80771 20 0.505 25% 2 of 8 100% 8 of 8 ha1g_00218 31 0.59 25% 2 of 8 100% 8 of 8 ha1p_80287 23 0.675 13% 1 of 8 100% 8 of 8 ha1p_40959 11 0.515 88% 7 of 8  88% 7 of 8 ha1p_69407 21 1.335 75% 6 of 8  88% 7 of 8 ha1p_29531 48 0.865 75% 6 of 8  88% 7 of 8 ha1p_40164 8 0.625 63% 5 of 8  88% 7 of 8 ha1p_108445 41 0.56 63% 5 of 8  88% 7 of 8 ha1p_70459 26 0.605 50% 4 of 8  88% 7 of 8 ha1p_35052 50 1.63 43% 3 of 7  88% 7 of 8 ha1g_02210 42 0.715 38% 3 of 8  88% 7 of 8 ha1g_02345 24 0.525 38% 3 of 8  86% 6 of 7 ha1p_101251 37 1.045 88% 7 of 8  75% 6 of 8 ha1p_67002 51 1.175 63% 5 of 8  75% 6 of 8 ha1p_88517 39 0.59 43% 3 of 7  75% 6 of 8 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 12 Sensitivity and Specificity of differentially methylated loci in head and neck tumors relative to adjacent histological normal head and neck tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_108445 41 2.695 89% 8 of 9 100% 5 of 5 ha1p_39189 2 1.035 78% 7 of 9 100% 5 of 5 ha1p_45173 19 1.195 78% 7 of 9 100% 5 of 5 ha1p_08347 14 0.94 67% 6 of 9 100% 5 of 5 ha1p_105474 33 2.15 67% 6 of 9 100% 5 of 5 ha1p_12075 46 0.795 67% 4 of 6 100% 4 of 4 ha1p_12646 53 2.28 67% 6 of 9 100% 5 of 5 ha1p_67625 30 0.505 63% 5 of 8 100% 5 of 5 ha1p_35052 50 1.575 63% 5 of 8 100% 5 of 5 ha1p_23178 9 1.715 56% 5 of 9 100% 5 of 5 ha1p_74707 34 0.76 56% 5 of 9 100% 5 of 5 ha1p_103872 43 2.535 56% 5 of 9 100% 5 of 5 ha1p_18292 45 1.725 56% 5 of 9 100% 4 of 4 ha1p_22519 47 2.27 56% 5 of 9 100% 5 of 5 ha1p_83841 6 1.69 50% 4 of 8 100% 5 of 5 ha1p_81149 5 2.98 44% 4 of 9 100% 5 of 5 ha1p_93325 35 2.145 44% 4 of 9 100% 5 of 5 ha1g_02210 42 1.11 38% 3 of 8 100% 5 of 5 ha1p_58853 49 2.815 38% 3 of 8 100% 5 of 5 ha1p_40959 11 1.42 33% 3 of 9 100% 5 of 5 ha1p_05406 22 0.685 33% 3 of 9 100% 4 of 4 ha1g_02345 24 0.54 33% 3 of 9 100% 5 of 5 ha1p_29531 48 1.465 33% 3 of 9 100% 5 of 5 ha1p_40164 8 1.63 22% 2 of 9 100% 5 of 5 ha1p_110107 17 0.62 22% 2 of 9 100% 5 of 5 ha1g_03099 29 0.68 22% 2 of 9 100% 4 of 4 ha1p_80287 23 1.625 13% 1 of 8 100% 5 of 5 ha1p_36172 25 0.73 11% 1 of 9 100% 5 of 5 ha1p_56412 44 1.53 89% 8 of 9  80% 4 of 5 ha1p_38705 7 0.855 75% 6 of 8  80% 4 of 5 ha1p_105937 27 0.545 67% 6 of 9  80% 4 of 5 ha1p_45580 52 1.315 67% 6 of 9  80% 4 of 5 ha1p_87540 16 0.865 63% 5 of 8  80% 4 of 5 ha1p_46057 10 1.18 56% 5 of 9  80% 4 of 5 ha1p_104423 12 0.675 56% 5 of 9  80% 4 of 5 ha1g_00847 13 1.125 56% 5 of 9  80% 4 of 5 ha1p_70459 26 0.555 56% 5 of 9  80% 4 of 5 ha1p_101161 36 0.765 44% 4 of 9  80% 4 of 5 ha1p_67002 51 1.67 44% 4 of 9  80% 4 of 5 ha1g_02416 15 0.71 38% 3 of 8  80% 4 of 5 ha1p_103824 40 0.525 33% 3 of 9  80% 4 of 5 ha1p_89799 18 0.69 22% 2 of 9  80% 4 of 5 ha1p_80771 20 0.86 11% 1 of 9  80% 4 of 5 ha1p_81674 4 1.15 78% 7 of 9  75% 3 of 4 ha1p_69407 21 1.315 67% 6 of 9  75% 3 of 4 ha1p_89099 28 0.59 67% 6 of 9  75% 3 of 4 ha1p_88517 39 0.985 86% 6 of 7  67% 2 of 3 ha1g_00644 3 0.52 89% 8 of 9  60% 3 of 5 ha1p_12535 32 0.95 78% 7 of 9  60% 3 of 5 ha1p_69214 38 1.39 78% 7 of 9  60% 3 of 5 ha1g_00681 1 1.045 89% 8 of 9  40% 2 of 5 ha1p_101251 37 0.78 67% 6 of 9  40% 2 of 5 ha1g_00218 31 — — — — — Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 13 Sensitivity and Specificity of differentially methylated loci in thyroid tumors relative to adjacent histological normal thyroid tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1g_02345 24 1.02 86% 6 of 7 100%  8 of 8 ha1g_02210 42 0.745 80% 4 of 5 100%  5 of 5 ha1p_05406 22 0.63 57% 4 of 7 100%  9 of 9 ha1p_88517 39 1.815 50% 4 of 8 100%  5 of 5 ha1p_56412 44 2.98 50% 4 of 8 100%  9 of 9 ha1p_45580 52 3.76 44% 4 of 9 100%  9 of 9 ha1g_02416 15 0.57 43% 3 of 7 100%  9 of 9 ha1p_36172 25 0.515 38% 3 of 8 100%  9 of 9 ha1g_00218 31 0.655 33% 3 of 9 100%  9 of 9 ha1p_105937 27 1.055 22% 2 of 9 100%  9 of 9 ha1g_00847 13 1.35 11% 1 of 9 100%  9 of 9 ha1p_80771 20 1.305 11% 1 of 9 100%  9 of 9 ha1p_80287 23 1.635 11% 1 of 9 100%  9 of 9 ha1g_03099 29 0.76 11% 1 of 9 100%  9 of 9 ha1p_108445 41 0.665 88% 7 of 8 89% 8 of 9 ha1p_89099 28 1.25 78% 7 of 9 89% 8 of 9 ha1p_29531 48 0.825 78% 7 of 9 89% 8 of 9 ha1p_67002 51 1.65 71% 5 of 7 89% 8 of 9 ha1p_39189 2 0.85 67% 6 of 9 89% 8 of 9 ha1g_00644 3 0.62 67% 6 of 9 89% 8 of 9 ha1p_22519 47 2.2 67% 6 of 9 89% 8 of 9 ha1p_38705 7 1.575 63% 5 of 8 89% 8 of 9 ha1p_89799 18 0.775 56% 5 of 9 89% 8 of 9 ha1p_74707 34 0.62 56% 5 of 9 89% 8 of 9 ha1p_93325 35 0.935 56% 5 of 9 89% 8 of 9 ha1p_23178 9 0.77 44% 4 of 9 89% 8 of 9 ha1p_110107 17 0.545 38% 3 of 8 89% 8 of 9 ha1p_101161 36 1.11 33% 3 of 9 89% 8 of 9 ha1p_103824 40 0.57 33% 3 of 9 89% 8 of 9 ha1p_12646 53 4.84 33% 3 of 9 89% 8 of 9 ha1p_81674 4 1.43 75% 6 of 8 88% 7 of 8 ha1p_67625 30 0.57 50% 3 of 6 88% 7 of 8 ha1p_69407 21 2.045 33% 3 of 9 88% 7 of 8 ha1g_00681 1 0.93 89% 8 of 9 78% 7 of 9 ha1p_83841 6 0.67 78% 7 of 9 78% 7 of 9 ha1p_46057 10 1.735 78% 7 of 9 78% 7 of 9 ha1p_40959 11 0.86 78% 7 of 9 78% 7 of 9 ha1p_45173 19 0.96 78% 7 of 9 78% 7 of 9 ha1p_101251 37 1.74 78% 7 of 9 78% 7 of 9 ha1p_58853 49 0.97 78% 7 of 9 78% 7 of 9 ha1p_103872 43 0.83 67% 6 of 9 78% 7 of 9 ha1p_12535 32 1.325 89% 8 of 9 67% 6 of 9 ha1p_12075 46 2.32 67% 6 of 9 67% 6 of 9 ha1p_104423 12 1.07 56% 5 of 9 67% 6 of 9 ha1p_105474 33 2.395 56% 5 of 9 67% 6 of 9 ha1p_08347 14 1.255 100%  9 of 9 56% 5 of 9 ha1p_69214 38 2.315 100%  9 of 9 56% 5 of 9 ha1p_18292 45 0.885 100%  8 of 8 56% 5 of 9 ha1p_81149 5 1.305 89% 8 of 9 56% 5 of 9 ha1p_40164 8 0.51 78% 7 of 9 56% 5 of 9 ha1p_87540 16 1.105 67% 6 of 9 56% 5 of 9 ha1p_35052 50 0.825 89% 8 of 9 44% 4 of 9 ha1p_70459 26 0.58 88% 7 of 8 43% 3 of 7 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 14 Sensitivity and Specificity of differentially methylated loci in bladder tumors relative to adjacent histological normal bladder tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1g_00681 1 0.865 100%  9 of 9 100% 8 of 8 ha1g_00644 3 1.245 100%  9 of 9 100% 9 of 9 ha1p_81674 4 1.895 100%  5 of 5 100% 6 of 6 ha1p_46057 10 1.255 100%  9 of 9 100% 9 of 9 ha1p_45173 19 1.39 100%  8 of 8 100% 9 of 9 ha1g_00847 13 2.315 89% 8 of 9 100% 8 of 8 ha1p_105937 27 0.89 89% 8 of 9 100% 9 of 9 ha1g_03099 29 1.155 89% 8 of 9 100% 7 of 7 ha1p_12535 32 0.745 89% 8 of 9 100% 8 of 8 ha1p_105474 33 0.96 89% 8 of 9 100% 9 of 9 ha1p_101161 36 1.185 89% 8 of 9 100% 9 of 9 ha1p_69214 38 2.12 89% 8 of 9 100% 9 of 9 ha1p_103872 43 1.045 89% 8 of 9 100% 9 of 9 ha1p_38705 7 0.54 88% 7 of 8 100% 9 of 9 ha1p_18292 45 2.025 88% 7 of 8 100% 8 of 8 ha1p_104423 12 1.54 78% 7 of 9 100% 9 of 9 ha1p_08347 14 2.225 78% 7 of 9 100% 8 of 8 ha1p_110107 17 0.995 78% 7 of 9 100% 9 of 9 ha1p_70459 26 2.27 78% 7 of 9 100% 9 of 9 ha1p_74707 34 1.24 78% 7 of 9 100% 9 of 9 ha1p_101251 37 1.655 78% 7 of 9 100% 9 of 9 ha1p_88517 39 3.035 78% 7 of 9 100% 9 of 9 ha1p_29531 48 0.625 78% 7 of 9 100% 9 of 9 ha1p_39189 2 0.525 75% 6 of 8 100% 6 of 6 ha1p_40164 8 1.815 67% 4 of 6 100% 8 of 8 ha1g_02416 15 2.01 67% 6 of 9 100% 8 of 8 ha1p_40959 11 0.54 56% 5 of 9 100% 7 of 7 ha1p_93325 35 1.845 56% 5 of 9 100% 9 of 9 ha1p_22519 47 1.265 56% 5 of 9 100% 9 of 9 ha1p_23178 9 1.66 50% 4 of 8 100% 9 of 9 ha1g_02345 24 0.665 44% 4 of 9 100% 8 of 8 ha1p_45580 52 1.22 22% 2 of 9 100% 8 of 8 ha1p_56412 44 1.725 100%  8 of 8  89% 8 of 9 ha1p_58853 49 1.125 100%  9 of 9  89% 8 of 9 ha1p_83841 6 0.8 89% 8 of 9  89% 8 of 9 ha1p_80771 20 0.67 89% 8 of 9  89% 8 of 9 ha1p_89099 28 1.08 89% 8 of 9  89% 8 of 9 ha1p_103824 40 0.575 89% 8 of 9  89% 8 of 9 ha1p_12075 46 0.705 88% 7 of 8  89% 8 of 9 ha1p_80287 23 0.595 78% 7 of 9  89% 8 of 9 ha1p_36172 25 0.63 75% 6 of 8  89% 8 of 9 ha1p_67625 30 0.765 75% 6 of 8  89% 8 of 9 ha1p_05406 22 2.03 56% 5 of 9  89% 8 of 9 ha1p_67002 51 2.215 56% 5 of 9  89% 8 of 9 ha1p_87540 16 1.395 89% 8 of 9  88% 7 of 8 ha1p_35052 50 0.65 56% 5 of 9  88% 7 of 8 ha1p_89799 18 1.05 89% 8 of 9  78% 7 of 9 ha1g_00218 31 0.83 89% 8 of 9  78% 7 of 9 ha1p_12646 53 0.79 78% 7 of 9  78% 7 of 9 ha1g_02210 42 1.145 60% 3 of 5  78% 7 of 9 ha1p_108445 41 1.99 100%  9 of 9  75% 6 of 8 ha1p_81149 5 0.615 100%  8 of 8  67% 6 of 9 ha1p_69407 21 1.185 100%  9 of 9  67% 6 of 9 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 15 Sensitivity and Specificity of differentially methylated loci in cervical tumors relative to adjacent histological normal cervical tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_83841 6 0.905 100%  9 of 9  100% 8 of 8 ha1p_23178 9 1 100%  9 of 9  100% 9 of 9 ha1p_74707 34 0.61 100%  9 of 9  100% 9 of 9 ha1p_108445 41 0.995 100%  10 of 10  100% 9 of 9 ha1p_40959 11 1.49 90% 9 of 10 100% 8 of 8 ha1g_00847 13 1.01 90% 9 of 10 100% 9 of 9 ha1p_69214 38 0.515 90% 9 of 10 100% 8 of 8 ha1g_00644 3 0.725 89% 8 of 9  100% 9 of 9 ha1p_40164 8 0.655 89% 8 of 9  100% 9 of 9 ha1p_103872 43 0.785 89% 8 of 9  100% 9 of 9 ha1p_110107 17 0.775 88% 7 of 8  100% 7 of 7 ha1p_39189 2 0.59 80% 8 of 10 100% 9 of 9 ha1p_46057 10 0.89 80% 8 of 10 100% 9 of 9 ha1p_45173 19 0.53 80% 8 of 10 100% 9 of 9 ha1p_58853 49 1.215 80% 8 of 10 100% 9 of 9 ha1p_88517 39 0.635 78% 7 of 9  100% 9 of 9 ha1p_80771 20 0.52 70% 7 of 10 100% 9 of 9 ha1p_105937 27 0.64 70% 7 of 10 100% 9 of 9 ha1p_101161 36 0.57 70% 7 of 10 100% 9 of 9 ha1g_02416 15 0.545 60% 6 of 10 100% 8 of 8 ha1p_103824 40 0.515 60% 6 of 10 100% 8 of 8 ha1p_38705 7 0.59 56% 5 of 9  100% 7 of 7 ha1g_02345 24 0.56 56% 5 of 9  100% 8 of 8 ha1p_104423 12 0.515 50% 5 of 10 100% 9 of 9 ha1p_36172 25 0.77 50% 5 of 10 100% 9 of 9 ha1p_70459 26 1.27 50% 5 of 10 100% 9 of 9 ha1p_05406 22 0.73 44% 4 of 9  100% 8 of 8 ha1p_87540 16 0.73 40% 4 of 10 100% 8 of 8 ha1p_89799 18 0.63 38% 3 of 8  100% 9 of 9 ha1p_08347 14 1.745 33% 3 of 9  100% 9 of 9 ha1g_03099 29 0.5 33% 3 of 9  100% 8 of 8 ha1p_89099 28 0.8 30% 3 of 10 100% 9 of 9 ha1p_67625 30 0.91 22% 2 of 9  100% 8 of 8 ha1p_80287 23 0.865 20% 2 of 10 100% 9 of 9 ha1g_00218 31 0.705 10% 1 of 10 100% 8 of 8 ha1p_12535 32 0.585 100%  10 of 10   89% 8 of 9 ha1p_93325 35 0.595 90% 9 of 10  89% 8 of 9 ha1p_29531 48 1.05 80% 8 of 10  89% 8 of 9 ha1p_101251 37 1.635 70% 7 of 10  89% 8 of 9 ha1p_81674 4 0.975 50% 4 of 8   89% 8 of 9 ha1p_67002 51 2.055 50% 5 of 10  89% 8 of 9 ha1g_02210 42 0.845 38% 3 of 8   89% 8 of 9 ha1p_45580 52 1.765 67% 6 of 9   88% 7 of 8 ha1p_12075 46 0.59 78% 7 of 9   83% 5 of 6 ha1p_81149 5 1.11 100%  9 of 9   78% 7 of 9 ha1p_105474 33 0.525 100%  10 of 10   78% 7 of 9 ha1p_22519 47 1.2 100%  10 of 10   78% 7 of 9 ha1p_12646 53 2.385 100%  10 of 10   78% 7 of 9 ha1p_18292 45 1.195 70% 7 of 10  75% 6 of 8 ha1p_56412 44 0.59 90% 9 of 10  67% 6 of 9 ha1g_00681 1 0.58 89% 8 of 9   67% 6 of 9 ha1p_35052 50 1.135 80% 8 of 10  56% 5 of 9 ha1p_69407 21 0.665 90% 9 of 10  44% 4 of 9 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 16 Sensitivity and Specificity of differentially methylated loci in colon tumors relative to adjacent histological normal colon tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_56412 44 1.855 88% 7 of 8 100%  8 of 8 ha1g_00644 3 1.46 86% 6 of 7 100%  8 of 8 ha1p_40959 11 2.16 71% 5 of 7 100%  6 of 6 ha1p_23178 9 1.805 63% 5 of 8 100%  8 of 8 ha1p_105937 27 1.425 63% 5 of 8 100%  8 of 8 ha1g_03099 29 0.63 63% 5 of 8 100%  8 of 8 ha1p_74707 34 2.08 63% 5 of 8 100%  8 of 8 ha1p_101161 36 0.65 63% 5 of 8 100%  8 of 8 ha1p_103824 40 0.605 63% 5 of 8 100%  8 of 8 ha1g_02210 42 0.82 63% 5 of 8 100%  8 of 8 ha1p_81674 4 4.63 50% 3 of 6 100%  8 of 8 ha1p_83841 6 2.175 50% 4 of 8 100%  7 of 7 ha1p_80771 20 1.31 50% 4 of 8 100%  8 of 8 ha1p_101251 37 1.29 50% 3 of 6 100%  8 of 8 ha1p_103872 43 2.36 50% 4 of 8 100%  8 of 8 ha1p_67002 51 1.57 50% 4 of 8 100%  8 of 8 ha1p_104423 12 0.575 43% 3 of 7 100%  8 of 8 ha1p_81149 5 2.82 38% 3 of 8 100%  8 of 8 ha1g_02345 24 0.58 38% 3 of 8 100%  8 of 8 ha1g_00218 31 1.745 38% 3 of 8 100%  8 of 8 ha1p_67625 30 0.515 25% 2 of 8 100%  8 of 8 ha1p_105474 33 1.99 100%  8 of 8 88% 7 of 8 ha1p_18292 45 1.545 100%  8 of 8 88% 7 of 8 ha1g_00681 1 0.705 88% 7 of 8 88% 7 of 8 ha1p_108445 41 2.175 88% 7 of 8 88% 7 of 8 ha1p_88517 39 1.645 86% 6 of 7 88% 7 of 8 ha1p_45173 19 1.145 75% 6 of 8 88% 7 of 8 ha1p_89099 28 2.22 75% 6 of 8 88% 7 of 8 ha1p_58853 49 1.85 75% 6 of 8 88% 7 of 8 ha1p_80287 23 1.485 63% 5 of 8 88% 7 of 8 ha1p_29531 48 1.3 63% 5 of 8 88% 7 of 8 ha1p_87540 16 1.52 86% 6 of 7 86% 6 of 7 ha1p_12535 32 0.93 75% 6 of 8 86% 6 of 7 ha1p_38705 7 1.01 50% 3 of 6 86% 6 of 7 ha1p_110107 17 0.74 100%  8 of 8 75% 6 of 8 ha1p_36172 25 0.53 100%  8 of 8 75% 6 of 8 ha1g_00847 13 2.085 88% 7 of 8 75% 6 of 8 ha1p_69214 38 2.085 88% 7 of 8 75% 6 of 8 ha1p_89799 18 1.295 75% 6 of 8 75% 6 of 8 ha1g_02416 15 1.295 63% 5 of 8 75% 6 of 8 ha1p_08347 14 1.415 88% 7 of 8 71% 5 of 7 ha1p_39189 2 0.575 86% 6 of 7 71% 5 of 7 ha1p_46057 10 1.06 100%  8 of 8 63% 5 of 8 ha1p_93325 35 1.07 100%  8 of 8 63% 5 of 8 ha1p_40164 8 0.815 75% 6 of 8 63% 5 of 8 ha1p_70459 26 1.21 75% 6 of 8 63% 5 of 8 ha1p_12075 46 0.935 75% 6 of 8 63% 5 of 8 ha1p_22519 47 1.15 50% 4 of 8 63% 5 of 8 ha1p_05406 22 1.64 75% 6 of 8 57% 4 of 7 ha1p_12646 53 2.16 67% 4 of 6 50% 4 of 8 ha1p_69407 21 0.565 100%  8 of 8 38% 3 of 8 ha1p_35052 50 0.565 100%  8 of 8 38% 3 of 8 ha1p_45580 52 1.505 100%  8 of 8 38% 3 of 8 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 17 Sensitivity and Specificity of differentially methylated loci in endometrial tumors relative to adjacent histological normal endometrial tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_39189 2 0.75 93% 13 of 14 100% 9 of 9 ha1g_00644 3 0.91 93% 13 of 14 100% 9 of 9 ha1p_83841 6 1.07 93% 13 of 14 100% 9 of 9 ha1p_103872 43 0.75 93% 13 of 14 100% 9 of 9 ha1p_56412 44 0.685 93% 13 of 14 100% 8 of 8 ha1p_12646 53 2.175 93% 13 of 14 100% 9 of 9 ha1p_40959 11 0.945 86% 12 of 14 100% 3 of 3 ha1p_58853 49 0.51 86% 12 of 14 100% 8 of 8 ha1p_12535 32 1.025 83% 10 of 12 100% 9 of 9 ha1p_18292 45 1.32 83% 10 of 12 100% 8 of 8 ha1p_46057 10 0.935 79% 11 of 14 100% 9 of 9 ha1p_45173 19 0.6 79% 11 of 14 100% 9 of 9 ha1p_105474 33 0.555 79% 11 of 14 100% 9 of 9 ha1p_101251 37 1.28 79% 11 of 14 100% 9 of 9 ha1p_22519 47 2.2 79% 11 of 14 100% 9 of 9 ha1p_29531 48 0.82 79% 11 of 14 100% 9 of 9 ha1p_87540 16 0.56 77% 10 of 13 100% 9 of 9 ha1g_02210 42 0.665 75% 6 of 8 100% 6 of 6 ha1g_00681 1 1.145 71% 10 of 14 100% 9 of 9 ha1p_23178 9 0.795 71% 10 of 14 100% 9 of 9 ha1p_110107 17 0.91 71% 5 of 7 100% 9 of 9 ha1p_105937 27 0.525 71% 10 of 14 100% 9 of 9 ha1p_101161 36 0.55 71% 10 of 14 100% 9 of 9 ha1p_69214 38 0.555 71% 10 of 14 100% 9 of 9 ha1p_88517 39 0.855 69%  9 of 13 100% 8 of 8 ha1p_108445 41 0.805 67%  8 of 12 100% 8 of 8 ha1p_38705 7 1.085 64%  9 of 14 100% 9 of 9 ha1p_40164 8 0.785 64%  9 of 14 100% 9 of 9 ha1p_104423 12 0.705 64%  9 of 14 100% 9 of 9 ha1p_89099 28 0.565 64%  9 of 14 100% 8 of 8 ha1p_05406 22 0.53 50%  7 of 14 100% 9 of 9 ha1p_67002 51 2.02 46%  6 of 13 100% 8 of 8 ha1p_36172 25 0.56 43%  6 of 14 100% 8 of 8 ha1g_03099 29 0.64 43%  6 of 14 100% 9 of 9 ha1p_74707 34 1.885 43%  6 of 14 100% 9 of 9 ha1p_35052 50 1.63 43%  6 of 14 100% 9 of 9 ha1p_45580 52 1.435 43%  6 of 14 100% 8 of 8 ha1g_02345 24 0.6 36%  5 of 14 100% 8 of 8 ha1p_67625 30 0.56 31%  4 of 13 100% 8 of 8 ha1p_80287 23 0.51 21%  3 of 14 100% 9 of 9 ha1g_00218 31 0.535 21%  3 of 14 100% 9 of 9 ha1p_103824 40 0.645 21%  3 of 14 100% 9 of 9 ha1p_81149 5 1.165 86% 12 of 14  89% 8 of 9 ha1p_81674 4 0.875 82%  9 of 11  89% 8 of 9 ha1g_00847 13 1.11 79% 11 of 14  89% 8 of 9 ha1p_69407 21 1.59 79% 11 of 14  89% 8 of 9 ha1p_93325 35 0.795 77% 10 of 13  89% 8 of 9 ha1p_70459 26 0.655 71% 10 of 14  89% 8 of 9 ha1p_80771 20 0.58 64%  9 of 14  89% 8 of 9 ha1p_08347 14 1.86 57%  8 of 14  89% 8 of 9 ha1g_02416 15 0.61 50%  7 of 14  89% 8 of 9 ha1p_89799 18 0.52 62%  8 of 13  88% 7 of 8 ha1p_12075 46 0.915 85% 11 of 13  78% 7 of 9 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 18 Sensitivity and Specificity of differentially methylated loci in esophageal tumors relative to adjacent histological normal esophageal tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_110107 17 0.515 67% 6 of 9 100%  9 of 9  ha1p_38705 7 2.58 43% 3 of 7 100%  9 of 9  ha1p_87540 16 1.21 33% 3 of 9 100%  9 of 9  ha1p_67625 30 0.665 33% 3 of 9 100%  9 of 9  ha1p_103824 40 0.72 13% 1 of 8 100%  10 of 10  ha1p_05406 22 0.71 11% 1 of 9 100%  10 of 10  ha1g_03099 29 0.6 11% 1 of 9 100%  10 of 10  ha1p_39189 2 1.165 89% 8 of 9 90% 9 of 10 ha1p_29531 48 1.27 78% 7 of 9 90% 9 of 10 ha1g_02416 15 0.65 38% 3 of 8 90% 9 of 10 ha1p_80771 20 0.675 22% 2 of 9 90% 9 of 10 ha1p_45173 19 0.985 100%  9 of 9 89% 8 of 9  ha1p_88517 39 1.14 100%  8 of 8 89% 8 of 9  ha1p_89799 18 0.53 14% 1 of 7 89% 8 of 9  ha1p_40959 11 1.52 78% 7 of 9 88% 7 of 8  ha1g_02210 42 0.905 33% 3 of 9 88% 7 of 8  ha1p_23178 9 1.645 100%  9 of 9 80% 8 of 10 ha1p_46057 10 0.92 100%  9 of 9 80% 8 of 10 ha1p_104423 12 0.65 100%  9 of 9 80% 8 of 10 ha1p_08347 14 0.75 100%  9 of 9 80% 8 of 10 ha1p_108445 41 1.715 100%  9 of 9 80% 8 of 10 ha1p_105937 27 0.855 89% 8 of 9 80% 8 of 10 ha1p_105474 33 1.655 89% 8 of 9 80% 8 of 10 ha1p_101161 36 0.785 89% 8 of 9 80% 8 of 10 ha1p_22519 47 1.77 89% 8 of 9 80% 8 of 10 ha1p_81149 5 2.02 78% 7 of 9 80% 8 of 10 ha1g_00644 3 0.905 75% 6 of 8 80% 8 of 10 ha1p_56412 44 0.74 67% 6 of 9 80% 8 of 10 ha1p_74707 34 1.035 56% 5 of 9 80% 8 of 10 ha1p_80287 23 0.62 22% 2 of 9 80% 8 of 10 ha1p_36172 25 0.6 22% 2 of 9 80% 8 of 10 ha1p_67002 51 1.925 78% 7 of 9 78% 7 of 9  ha1p_89099 28 0.6 56% 5 of 9 78% 7 of 9  ha1p_18292 45 1.18 89% 8 of 9 75% 6 of 8  ha1p_58853 49 1.105 100%  9 of 9 70% 7 of 10 ha1p_83841 6 1.125 89% 8 of 9 70% 7 of 10 ha1p_12535 32 0.78 89% 8 of 9 70% 7 of 10 ha1p_101251 37 1.025 78% 7 of 9 70% 7 of 10 ha1g_00847 13 0.825 100%  9 of 9 67% 6 of 9  ha1p_69407 21 1.005 100%  9 of 9 60% 6 of 10 ha1p_93325 35 0.93 89% 8 of 9 60% 6 of 10 ha1g_00681 1 0.52 67% 6 of 9 60% 6 of 10 ha1p_70459 26 0.515 100%  9 of 9 56% 5 of 9  ha1p_40164 8 0.645 56% 5 of 9 56% 5 of 9  ha1p_69214 38 0.73 100%  9 of 9 50% 5 of 10 ha1p_103872 43 1.5 100%  9 of 9 50% 5 of 10 ha1p_81674 4 0.645 75% 6 of 8 44% 4 of 9  ha1p_12075 46 1.02 100%  7 of 7 40% 4 of 10 ha1p_45580 52 0.73 100%  9 of 9 40% 4 of 10 ha1p_35052 50 1.84 100%  9 of 9 30% 3 of 10 ha1p_12646 53 1.085 100%  9 of 9 30% 3 of 10 ha1g_02345 24 0.83 89% 8 of 9 10% 1 of 10 ha1g_00218 31 — — — — — Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 19 Sensitivity and Specificity of differentially methylated loci in prostate tumors relative to adjacent histological normal prostate tissue. Pos. Neg. Feature Locus Thresh- Sensi- of Speci- of ID Number old tivity Total ficity Total ha1p_38705 7 0.595 78% 7 of 9 100%  9 of 9 ha1p_36172 25 1 67% 6 of 9 100%  9 of 9 ha1p_56412 44 1.63 67% 6 of 9 100%  9 of 9 ha1p_45580 52 1.785 67% 6 of 9 100%  9 of 9 ha1p_12646 53 3.13 67% 6 of 9 100%  9 of 9 ha1p_67002 51 2.05 56% 5 of 9 100%  9 of 9 ha1g_02416 15 0.845 44% 4 of 9 100%  9 of 9 ha1p_103824 40 0.59 22% 2 of 9 100%  9 of 9 ha1p_104423 12 0.655 11% 1 of 9 100%  9 of 9 ha1p_45173 19 0.53 11% 1 of 9 100%  9 of 9 ha1g_03099 29 2.165 11% 1 of 9 100%  8 of 8 ha1p_89799 18 0.51 75% 6 of 8 89% 8 of 9 ha1g_02210 42 0.715 75% 6 of 8 89% 8 of 9 ha1p_46057 10 0.605 67% 6 of 9 89% 8 of 9 ha1p_108445 41 0.83 67% 6 of 9 89% 8 of 9 ha1p_80287 23 0.545 63% 5 of 8 89% 8 of 9 ha1p_87540 16 0.54 56% 5 of 9 89% 8 of 9 ha1p_74707 34 1.425 56% 5 of 9 89% 8 of 9 ha1p_18292 45 1.5 56% 5 of 9 89% 8 of 9 ha1p_35052 50 0.73 56% 5 of 9 89% 8 of 9 ha1g_02345 24 0.635 44% 4 of 9 89% 8 of 9 ha1p_88517 39 0.885 44% 4 of 9 89% 8 of 9 ha1p_12075 46 1.15 44% 4 of 9 89% 8 of 9 ha1p_69407 21 0.75 38% 3 of 8 89% 8 of 9 ha1p_80771 20 0.585 33% 3 of 9 89% 8 of 9 ha1p_05406 22 0.745 33% 3 of 9 89% 8 of 9 ha1p_70459 26 0.615 33% 3 of 9 89% 8 of 9 ha1g_00218 31 0.675 11% 1 of 9 89% 8 of 9 ha1p_69214 38 1.175 88% 7 of 8 88% 7 of 8 ha1p_67625 30 0.53 33% 3 of 9 88% 7 of 8 ha1p_110107 17 1.15  0% 0 of 8 88% 7 of 8 ha1p_105937 27 0.545 100%  9 of 9 78% 7 of 9 ha1p_103872 43 1.64 100%  9 of 9 78% 7 of 9 ha1p_22519 47 1.38 100%  9 of 9 78% 7 of 9 ha1p_29531 48 0.77 100%  9 of 9 78% 7 of 9 ha1p_83841 6 0.515 89% 8 of 9 78% 7 of 9 ha1p_105474 33 1.35 89% 8 of 9 78% 7 of 9 ha1p_101161 36 0.875 89% 8 of 9 78% 7 of 9 ha1g_00644 3 0.56 88% 7 of 8 78% 7 of 9 ha1p_39189 2 0.53 78% 7 of 9 78% 7 of 9 ha1p_81149 5 1.17 78% 7 of 9 78% 7 of 9 ha1p_08347 14 1.405 67% 6 of 9 78% 7 of 9 ha1p_12535 32 1.19 67% 6 of 9 78% 7 of 9 ha1p_40164 8 0.7 63% 5 of 8 78% 7 of 9 ha1p_23178 9 1.575 56% 5 of 9 78% 7 of 9 ha1p_89099 28 0.5 56% 5 of 9 78% 7 of 9 ha1p_40959 11 0.745 100%  8 of 8 67% 6 of 9 ha1p_93325 35 0.535 100%  9 of 9 67% 6 of 9 ha1p_101251 37 1.27 100%  8 of 8 67% 6 of 9 ha1p_81674 4 1.245 78% 7 of 9 67% 6 of 9 ha1g_00847 13 0.63 78% 7 of 9 67% 6 of 9 ha1g_00681 1 0.55 50% 4 of 8 67% 6 of 9 ha1p_58853 49 0.835 100%  9 of 9 22% 2 of 9 Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues. Sensitivity: % of positive (i.e. methylation score above Threshold) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Specificity: % of negative (i.e. methylation score below Threshold) adjacent normal samples. Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

TABLE 20 Frequency of methylation of each locus in melanoma tumors. Feature ID Locus Number Sensitivity Pos. of Total ha1p_81149 5 100% 7 of 7 ha1p_38705 7 100% 5 of 5 ha1p_23178 9 100% 7 of 7 hal p_45173 19 100% 6 of 6 ha1p_12535 32 100% 7 of 7 ha1p_69214 38 100% 7 of 7 ha1p_108445 41 100% 7 of 7 ha1p_45580 52 100% 7 of 7 ha1p_12646 53 100% 7 of 7 ha1g_00644 3  86% 6 of 7 ha1p_46057 10  86% 6 of 7 ha1p_40959 11  86% 6 of 7 ha1p_104423 12  86% 6 of 7 ha1p_105474 33  86% 6 of 7 ha1p_93325 35  86% 6 of 7 ha1p_103872 43  86% 6 of 7 ha1p_56412 44  86% 6 of 7 ha1p_18292 45  86% 6 of 7 ha1p_22519 47  86% 6 of 7 ha1p_29531 48  86% 6 of 7 ha1p_58853 49  86% 6 of 7 ha1p_35052 50  86% 6 of 7 ha1p_67002 51  86% 6 of 7 ha1p_81674 4  83% 5 of 6 ha1p_69407 21  83% 5 of 6 ha1p_12075 46  80% 4 of 5 ha1g_00681 1  71% 5 of 7 ha1p_39189 2  71% 5 of 7 ha1p_83841 6  71% 5 of 7 ha1p_40164 8  71% 5 of 7 ha1g_00847 13  71% 5 of 7 ha1p_87540 16  57% 4 of 7 ha1p_101251 37  57% 4 of 7 ha1p_88517 39  57% 4 of 7 ha1p_08347 14  50% 3 of 6 ha1p_05406 22  43% 3 of 7 ha1p_105937 27  43% 3 of 7 ha1p_89099 28  43% 3 of 7 ha1g_02210 42  43% 3 of 7 ha1p_89799 18  40% 2 of 5 ha1p_80287 23  29% 2 of 7 ha1p_36172 25  29% 2 of 7 ha1p_70459 26  29% 2 of 7 ha1g_03099 29  29% 2 of 7 ha1p_101161 36  29% 2 of 7 ha1p_110107 17  14% 1 017 ha1p_80771 20  14% 1 of 7 ha1p_67625 30  14% 1 of 7 ha1p_74707 34  14% 1 of 7 ha1g_02416 15  0% 0 of 7 ha1g_02345 24  0% 0 of 7 ha1g_00218 31  0% 0 of 7 ha1p_103824 40  0% 0 of 7 Sensitivity: % of positive (i.e. methylation score above 1.0) tumors. Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed. Note that adjacent histology normal or normal skin samples were not available for analysis. Threshold for a positive methylation score was set at an average dCt of 1.0.

Although the invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to one of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

All publications, databases, Genbank sequences, patents, and patent applications cited in this specification are herein incorporated by reference as if each was specifically and individually indicated to be incorporated by reference. 

1. A method for determining the methylation status of an individual, the method comprising: obtaining a biological sample from an individual; and determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90% identical to a sequence selected from the group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and
 265. 2. The method of claim 1, wherein the determining step comprises determining the methylation status of at least one cytosine in the DNA region corresponding to a nucleotide in a biomarker in the DNA region, wherein the biomarker is a sequence selected from the group consisting of SEQ ID NOs:160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, and
 212. 3. The method of claim 2, wherein the determining step comprises determining the methylation status of the DNA region corresponding to the biomarker.
 4. The method of claim 1, wherein the sample is from blood serum, blood plasma, fine needle aspirate of the breast, biopsy of the breast, ductal fluid, or ductal lavage.
 5. The method of claim 1, wherein the methylation status of at least one cytosine is compared to the methylation status of a control locus.
 6. The method of claim 5, wherein the control locus is an endogenous control.
 7. The method of claim 5, wherein the control locus is an exogenous control.
 8. The method of claim 1, wherein the determining step comprises determining the methylation status of at least one cytosine in at least two DNA regions selected from the group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and
 265. 9. A method for determining the presence or absence of cancer in an individual, the method comprising: a) determining the methylation status of at least one cytosine within a DNA region in a sample from an individual where the DNA region is at least 90% identical to a sequence selected from the group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265; b) comparing the methylation status of the at least one cytosine to a threshold value for the at least one cytosine, wherein the threshold value distinguishes between individuals with and without cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of cancer in the individual.
 10. The method of claim 9, wherein DNA region is a sequence selected from the group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and
 265. 11. The method of claim 9, wherein the determining step comprises determining the methylation status of at least one cytosine in the DNA region corresponding to a nucleotide in a biomarker, wherein the biomarker is at least 90% identical to a sequence selected from the group consisting of SEQ ID NOs: 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, and
 212. 12. The method of claim 11, wherein the determining step comprises determining the methylation status of the DNA region corresponding to the biomarker.
 13. The method of claim 9, wherein the sample is from blood serum, blood plasma, fine needle aspirate of the breast, biopsy of the breast, ductal fluid, or ductal lavage.
 14. The method of claim 9, wherein the methylation status of at least one biomarker from the list is compared to the methylation value of a control locus.
 15. The method of claim 14, wherein the control locus is an endogenous control.
 16. The method of claim 14, wherein the control locus is an exogenous control.
 17. The method of claim 9, wherein the determining step comprises determining the methylation status of at least one cytosine from at least two DNA regions.
 18. A computer-implemented method for determining the presence or absence of cancer in an individual, the method comprising: receiving, at a host computer, a methylation value representing the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90% identical to a sequence selected from the group consisting of SEQ ID NOs: 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, and 265; and comparing, in the host computer, the methylation value to a threshold value, wherein the threshold value distinguishes between individuals with and without cancer, wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of cancer in the individual.
 19. The method of claim 18, wherein the receiving step comprises receiving at least two methylation values, the two methylation values representing the methylation status of at least one cytosine biomarkers from two different DNA regions; and the comparing step comprises comparing the methylation values to one or more threshold value(s) wherein the threshold value distinguishes between individuals with and without cancer, wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of cancer in the individual. 